This is an R Markdown Notebook for analysis using data on the DC Bus System (WMATA Metrobus). The data were obtained here:
https://planitmetro.com/2016/11/16/data-download-metrobus-vehicle-location-data/

These analyses coincide with a Shiny dashboard on waitimes found here:
https://mdat.shinyapps.io/DCMetroBus_WaitTimes_20170319/

Load the packages to be used.

# install.packages('rgeos', type='source')
# install.packages('rgdal', type='source')
# install.packages("NbClust")
library("jsonlite")           # manipulating JSON files for zip codes
library("sqldf")              # sql-based data manipulation
library("tcltk")
library("tidyr")              # data manipulation
library("plyr")               # data manipulation
library("dplyr")              # data manipulation
library("magrittr")           # data manipulation (piping data)
library("stringr")            # string manipulation
library("data.table")         # used in testing data manipulation for speed increases
library("lubridate")          # date manipulation
library("geosphere")          # calculating Haversine distance
library("ggplot2")            # general plotting
library("ggvis")              # general plotting
library("rbokeh")             # general plotting
library("ggmap")              # general plotting of maps
library("rgdal")              # used in plotting shapefiles
library("broom")              # used in plotting shapefiles
library("maptools")           # used in plotting shapefiles
library("rgeos")              # used in plotting shapefiles
library("caret")              # used in PCA
library("cluster")            # used for clustering
library("fpc")                # used for clustering
library("dbscan")             # used for clustering
library("NbClust")            # used for clustering
library("factoextra")         # plotting clusters

Session Info.

sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.12.4 (unknown)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] tcltk     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] factoextra_1.0.4  NbClust_3.0       dbscan_1.1-1      fpc_2.1-10       
 [5] cluster_2.0.6     caret_6.0-73      lattice_0.20-35   rgeos_0.3-23     
 [9] maptools_0.9-2    broom_0.4.2       rgdal_1.2-5       ggmap_2.7        
[13] rbokeh_0.5.0      ggvis_0.4.3       ggplot2_2.2.1     geosphere_1.5-5  
[17] sp_1.2-4          lubridate_1.6.0   data.table_1.10.4 stringr_1.2.0    
[21] magrittr_1.5      dplyr_0.5.0       plyr_1.8.4        tidyr_0.6.1      
[25] sqldf_0.4-10      RSQLite_1.1-2     gsubfn_0.6-6      proto_1.0.0      
[29] jsonlite_1.4     

loaded via a namespace (and not attached):
 [1] nlme_3.1-131       bitops_1.0-6       pbkrtest_0.4-7     httr_1.2.1        
 [5] rprojroot_1.2      prabclus_2.2-6     tools_3.2.3        backports_1.0.5   
 [9] R6_2.2.0           DBI_0.6-1          lazyeval_0.2.0     mgcv_1.8-17       
[13] colorspace_1.3-2   trimcluster_0.1-2  nnet_7.3-12        mnormt_1.5-5      
[17] curl_2.4           chron_2.3-50       quantreg_5.29      SparseM_1.76      
[21] diptest_0.75-7     scales_0.4.1       DEoptimR_1.0-8     hexbin_1.27.1     
[25] mvtnorm_1.0-6      psych_1.7.3.21     robustbase_0.92-7  digest_0.6.12     
[29] foreign_0.8-67     minqa_1.2.4        rmarkdown_1.4      base64enc_0.1-3   
[33] jpeg_0.1-8         htmltools_0.3.5    lme4_1.1-12        maps_3.1.1        
[37] htmlwidgets_0.8    gistr_0.3.6        pryr_0.1.2         shiny_1.0.1       
[41] mclust_5.2.3       ModelMetrics_1.1.0 car_2.1-4          modeltools_0.2-21 
[45] Matrix_1.2-8       Rcpp_0.12.10       munsell_0.4.3      stringi_1.1.5     
[49] yaml_2.1.14        MASS_7.3-45        flexmix_2.3-13     grid_3.2.3        
[53] ggrepel_0.6.9      parallel_3.2.3     splines_3.2.3      mapproj_1.2-4     
[57] knitr_1.15.1       rjson_0.2.15       reshape2_1.4.2     codetools_0.2-15  
[61] stats4_3.2.3       evaluate_0.10      png_0.1-7          nloptr_1.0.4      
[65] httpuv_1.3.3       foreach_1.4.3      MatrixModels_0.4-1 RgoogleMaps_1.4.1 
[69] gtable_0.2.0       kernlab_0.9-25     assertthat_0.1     mime_0.5          
[73] xtable_1.8-2       rsconnect_0.7      class_7.3-14       tibble_1.3.0      
[77] iterators_1.0.8    memoise_1.0.0     
Get the Bus data.

First let’s update the directory for this Chunk to the location where the raw data files are saved.

Then, actually get the data.

setwd(paste0(BasePath, "DCMetroBus/Bus AVL Oct 2016")
     )
for (i in 3:7){
  assign(paste0("Oct0", i, "Raw"),
         read.delim(paste0("2016100", i, "MetrobusAVL.txt"),
                    sep = "\t",
                    header = TRUE,
                    na.strings = NULL
                   )
        )
  
  message("Oct0", i, "Raw")
 
  str(get(paste0("Oct0", i, "Raw")
         )
     )
  }
Oct03Raw
'data.frame':   620274 obs. of  17 variables:
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : Factor w/ 266 levels "10A","10B","10E",..: 224 224 224 224 224 224 224 224 224 224 ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 6 6 ...
 $ Route_Direction  : Factor w/ 11 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence    : int  7 7 6 3 2 8 1 1 2 3 ...
 $ Stop_ID          : Factor w/ 10552 levels "","1000001","1000003",..: 9682 9682 9683 9641 9640 8136 9668 9668 9796 9795 ...
 $ Stop_Desc        : Factor w/ 7740 levels "10TH ST + MICHIGAN AVE",..: 1346 1346 7417 7418 1346 2940 2939 2939 6926 6929 ...
 $ Event_Type       : int  4 5 4 4 4 3 3 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 2 3 3 3 1 1 3 3 3 ...
 $ Event_Time       : Factor w/ 75354 levels "10-3-16 1:00:00 AM",..: 47380 47506 47740 47814 47864 48244 48302 48540 49086 49190 ...
 $ Departure_Time   : Factor w/ 75396 levels "10-3-16 1:00:00 AM",..: 47406 47554 47766 47840 47890 48270 48536 48566 49112 49216 ...
 $ Dwell_Time       : int  0 11 0 0 0 0 104 0 0 0 ...
 $ Delta_Time       : int  -177 -27 24 165 25 73 719 0 74 76 ...
 $ Odometer_Distance: int  43543 43543 45139 46418 50115 51074 51303 53836 55633 56163 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 253 97 276 15 119 100 89 274 104 ...
Oct04Raw
'data.frame':   623427 obs. of  17 variables:
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : Factor w/ 266 levels "10A","10B","10E",..: 225 225 225 225 225 225 225 225 225 225 ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 6 6 6 6 6 6 6 ...
 $ Route_Direction  : Factor w/ 9 levels "ANTICLKW","CLOCKWIS",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ Stop_Sequence    : int  1 1 8 1 1 1 1 3 2 5 ...
 $ Stop_ID          : Factor w/ 10555 levels "","1000001","1000003",..: 9671 9671 8138 8138 8138 8138 8138 9798 9799 9638 ...
 $ Stop_Desc        : Factor w/ 7717 levels "10TH ST + MICHIGAN AVE",..: 2939 2939 2940 2940 2940 2940 2940 6906 6903 4205 ...
 $ Event_Type       : int  3 4 3 3 3 5 5 4 5 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 1 1 2 2 3 2 3 ...
 $ Event_Time       : Factor w/ 77713 levels "10-4-16 1:00:00 AM",..: 49126 49240 50858 50908 50976 51116 51172 51714 51842 51940 ...
 $ Departure_Time   : Factor w/ 77739 levels "10-4-16 1:00:00 AM",..: 49209 49251 50869 50957 50987 51165 51185 51725 51933 51951 ...
 $ Dwell_Time       : int  79 0 59 19 59 19 1 0 40 0 ...
 $ Delta_Time       : int  35 36 36 246 244 255 264 159 129 139 ...
 $ Odometer_Distance: int  56958 60750 69747 69971 69747 71136 71177 76520 77425 78353 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  58 89 82 76 79 301 274 104 310 2 ...
Oct05Raw
'data.frame':   630900 obs. of  17 variables:
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : Factor w/ 266 levels "10A","10B","10E",..: 224 224 224 224 224 224 224 224 224 224 ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Route_Direction  : Factor w/ 11 levels "ANTICLKW","CLOCKWIS",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ Stop_Sequence    : int  1 1 3 4 5 4 3 3 7 7 ...
 $ Stop_ID          : Factor w/ 10543 levels "","1000001","1000003",..: 9659 9659 9632 9633 9624 9633 9632 9632 9673 9673 ...
 $ Stop_Desc        : Factor w/ 7725 levels "10TH ST + MICHIGAN AVE",..: 2946 2946 7403 7401 7401 7401 7403 7403 1346 1346 ...
 $ Event_Type       : int  3 5 4 4 3 4 3 3 5 5 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 1 2 3 3 1 3 1 1 2 2 ...
 $ Event_Time       : Factor w/ 77725 levels "10-5-16 1:00:00 AM",..: 49279 49371 49899 49953 49993 50135 50221 50493 50783 50987 ...
 $ Departure_Time   : Factor w/ 77716 levels "10-5-16 1:00:00 AM",..: 49257 49353 49877 49931 49997 50113 50421 50489 50767 50999 ...
 $ Dwell_Time       : int  189 2 0 0 13 0 111 9 3 17 ...
 $ Delta_Time       : int  4 78 -114 -3 19 93 297 191 382 499 ...
 $ Odometer_Distance: int  37932 38703 44242 44327 44645 45927 46733 47077 50461 51916 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  316 104 276 246 229 246 345 207 109 305 ...
Oct06Raw
'data.frame':   621948 obs. of  17 variables:
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : Factor w/ 265 levels "10A","10B","10E",..: 224 224 224 224 224 224 224 224 224 224 ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Route_Direction  : Factor w/ 9 levels "ANTICLKW","CLOCKWIS",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ Stop_Sequence    : int  1 1 7 6 5 4 3 7 7 7 ...
 $ Stop_ID          : Factor w/ 10562 levels "","1000001","1000003",..: 9678 9678 9692 9693 9643 9652 9651 9692 9692 9692 ...
 $ Stop_Desc        : Factor w/ 7723 levels "10TH ST + MICHIGAN AVE",..: 2937 2937 1342 7400 7399 7399 7401 1342 1342 1342 ...
 $ Event_Type       : int  3 5 4 5 3 4 3 4 5 5 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 1 2 3 2 1 3 1 3 2 2 ...
 $ Event_Time       : Factor w/ 77758 levels "10-6-16 1:00:00 AM",..: 49294 49384 49982 49998 50058 50186 50270 50518 51002 51064 ...
 $ Departure_Time   : Factor w/ 77792 levels "10-6-16 1:00:00 AM",..: 49305 49399 49993 50023 50091 50197 50487 50529 51065 51079 ...
 $ Dwell_Time       : int  148 2 0 7 11 0 103 0 26 2 ...
 $ Delta_Time       : int  -6 64 -87 -93 31 104 303 175 497 504 ...
 $ Odometer_Distance: int  37950 38726 44130 44197 44592 45935 46739 51826 51826 51838 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  284 71 199 164 223 246 343 199 306 320 ...
Oct07Raw
'data.frame':   622894 obs. of  17 variables:
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : Factor w/ 266 levels "10A","10B","10E",..: 224 224 224 224 224 224 224 224 224 224 ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 6 6 6 6 6 6 7 7 ...
 $ Route_Direction  : Factor w/ 7 levels "ANTICLKW","CLOCKWIS",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ Stop_Sequence    : int  1 1 2 3 3 5 6 7 1 2 ...
 $ Stop_ID          : Factor w/ 10556 levels "","1000001","1000003",..: 9672 9672 9800 9799 9799 9639 9640 9641 9641 9642 ...
 $ Stop_Desc        : Factor w/ 7699 levels "10TH ST + MICHIGAN AVE",..: 2930 2930 6886 6889 6889 4196 4199 6887 6887 4198 ...
 $ Event_Type       : int  3 4 3 4 5 4 4 3 3 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 3 2 3 3 1 1 3 ...
 $ Event_Time       : Factor w/ 77562 levels "10-7-16 1:00:00 AM",..: 49134 49136 51718 51756 51888 51934 52018 52044 52130 52288 ...
 $ Departure_Time   : Factor w/ 77649 levels "10-7-16 1:00:00 AM",..: 49193 49195 51779 51815 51953 51993 52077 52103 52189 52347 ...
 $ Dwell_Time       : int  153 0 1 0 3 0 0 120 120 0 ...
 $ Delta_Time       : int  57 56 165 270 197 201 181 189 235 288 ...
 $ Odometer_Distance: int  37846 42154 56018 56611 57411 58341 59084 59787 59787 60252 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  98 89 201 104 26 2 1 247 247 182 ...

Put the daily data together.

AllDays <- bind_rows(list(Oct03Raw, Oct04Raw, Oct05Raw, Oct06Raw, Oct07Raw),
                     .id = c("group")
                    )
# dim(AllDays)
str(AllDays)
'data.frame':   3119443 obs. of  18 variables:
 $ group            : chr  "1" "1" "1" "1" ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 6 6 ...
 $ Route_Direction  : chr  "LOOP" "LOOP" "LOOP" "LOOP" ...
 $ Stop_Sequence    : int  7 7 6 3 2 8 1 1 2 3 ...
 $ Stop_ID          : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ Event_Type       : int  4 5 4 4 4 3 3 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 2 3 3 3 1 1 3 3 3 ...
 $ Event_Time       : chr  "10-3-16 6:06:47 AM" "10-3-16 6:07:50 AM" "10-3-16 6:09:47 AM" "10-3-16 6:10:24 AM" ...
 $ Departure_Time   : chr  "10-3-16 6:06:47 AM" "10-3-16 6:08:01 AM" "10-3-16 6:09:47 AM" "10-3-16 6:10:24 AM" ...
 $ Dwell_Time       : int  0 11 0 0 0 0 104 0 0 0 ...
 $ Delta_Time       : int  -177 -27 24 165 25 73 719 0 74 76 ...
 $ Odometer_Distance: int  43543 43543 45139 46418 50115 51074 51303 53836 55633 56163 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 253 97 276 15 119 100 89 274 104 ...

Deleting old data frames.

for (i in 3:7){
  rm(list = ls(pattern = paste0("Oct0", i, "Raw")
              )
    )
  
  message("Deleting Oct0", i, "Raw")
  }
Deleting Oct03Raw
Deleting Oct04Raw
Deleting Oct05Raw
Deleting Oct06Raw
Deleting Oct07Raw

Updating variable types.

Then, sorting the data and adding a RowNumber (to be used for identifying rows later in the analyses.)

rm(i)
AllDays$group <- factor(AllDays$group)
AllDays$Route_Direction <- factor(AllDays$Route_Direction)
AllDays$Event_Time <- as.POSIXct(AllDays$Event_Time, format = "%m-%d-%y %I:%M:%S %p")
AllDays$Departure_Time <- as.POSIXct(AllDays$Departure_Time, format = "%m-%d-%y %I:%M:%S %p")
str(AllDays)
'data.frame':   3119443 obs. of  18 variables:
 $ group            : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 6 6 ...
 $ Route_Direction  : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence    : int  7 7 6 3 2 8 1 1 2 3 ...
 $ Stop_ID          : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ Event_Type       : int  4 5 4 4 4 3 3 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 2 3 3 3 1 1 3 3 3 ...
 $ Event_Time       : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:07:50" ...
 $ Departure_Time   : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:08:01" ...
 $ Dwell_Time       : int  0 11 0 0 0 0 104 0 0 0 ...
 $ Delta_Time       : int  -177 -27 24 165 25 73 719 0 74 76 ...
 $ Odometer_Distance: int  43543 43543 45139 46418 50115 51074 51303 53836 55633 56163 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 253 97 276 15 119 100 89 274 104 ...
AllDays_Sorted <- arrange(AllDays,
                          Bus_ID,
                          Event_Time
                         ) %>% 
  mutate(RowNum_OG = row_number() # this is useful in identify the row later on
        )
rm(AllDays)
str(AllDays_Sorted)
'data.frame':   3119443 obs. of  19 variables:
 $ group            : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 6 6 ...
 $ Route_Direction  : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence    : int  7 7 6 3 2 8 1 1 2 3 ...
 $ Stop_ID          : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ Event_Type       : int  4 5 4 4 4 3 3 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 2 3 3 3 1 1 3 3 3 ...
 $ Event_Time       : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:07:50" ...
 $ Departure_Time   : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:08:01" ...
 $ Dwell_Time       : int  0 11 0 0 0 0 104 0 0 0 ...
 $ Delta_Time       : int  -177 -27 24 165 25 73 719 0 74 76 ...
 $ Odometer_Distance: int  43543 43543 45139 46418 50115 51074 51303 53836 55633 56163 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 253 97 276 15 119 100 89 274 104 ...
 $ RowNum_OG        : int  1 2 3 4 5 6 7 8 9 10 ...
# View(head(AllDays_Sorted, 100))

Inspecting the values of Stop_ID, and finding that it can take the values “” (blank) and “NULL”.

Creating a table of distinct Stop_Desc values when Stop_ID is “” (blank) or “NULL”.

StopID_New <- filter(AllDays_Sorted,
                     is.na(Stop_ID) |
                       Stop_ID == "" |
                       Stop_ID == "NULL"
                    ) %>% 
  select(Stop_ID, Stop_Desc) %>% 
  distinct() %>% 
  arrange(Stop_ID, Stop_Desc) %>% 
  mutate(StopID_New = 1:nrow(.)
        )
View(StopID_New)
StopID_New

Creating a full updated table by filling in StopID_New for when Stop_ID is “” (blank) or NULL.

AllDays_StopIDNew <- left_join(AllDays_Sorted,
                               select(StopID_New,
                                      Stop_Desc,
                                      StopID_New
                                     ),
                               by = c("Stop_Desc" = "Stop_Desc")
                              ) %>% 
  mutate(StopID_Clean = ifelse(is.na(StopID_New),
                               Stop_ID,
                               StopID_New
                              ),
         StopID_Indicator = factor(ifelse(is.na(StopID_New),
                                          "ID_OK",
                                          "ID_Bad"
                                         )
                                  )
        )
rm(StopID_New)
rm(AllDays_Sorted)
str(AllDays_StopIDNew)
'data.frame':   3119443 obs. of  22 variables:
 $ group            : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 6 6 ...
 $ Route_Direction  : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence    : int  7 7 6 3 2 8 1 1 2 3 ...
 $ Stop_ID          : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ Event_Type       : int  4 5 4 4 4 3 3 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 2 3 3 3 1 1 3 3 3 ...
 $ Event_Time       : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:07:50" ...
 $ Departure_Time   : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:08:01" ...
 $ Dwell_Time       : int  0 11 0 0 0 0 104 0 0 0 ...
 $ Delta_Time       : int  -177 -27 24 165 25 73 719 0 74 76 ...
 $ Odometer_Distance: int  43543 43543 45139 46418 50115 51074 51303 53836 55633 56163 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 253 97 276 15 119 100 89 274 104 ...
 $ RowNum_OG        : int  1 2 3 4 5 6 7 8 9 10 ...
 $ StopID_New       : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean     : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ StopID_Indicator : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
# View(tail(AllDays_StopIDNew, 500))
# View(filter(AllDays_StopIDNew,
#             Stop_Desc == "METROWAY ANNNOUCEMNT CORR"
#            )
#     )

Lat Long stats for pulling in Zip codes later.

LL_Stats <- group_by(AllDays_StopIDNew,
                     StopID_Clean
                    ) %>% 
  summarise(Lat_Mean = mean(Latitude, na.rm = TRUE),
            Lat_Med = median(Latitude, na.rm = TRUE),
            Lng_Mean = mean(Longitude, na.rm = TRUE),
            Lng_Med = median(Longitude, na.rm = TRUE)
           ) %>% 
  mutate(Lat_MeaLessMed = Lat_Mean - Lat_Med,
         Lng_MeaLessMed = Lng_Mean - Lng_Med,
         RowNum = row_number()
        )
str(LL_Stats)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   10588 obs. of  8 variables:
 $ StopID_Clean  : chr  "1" "10" "1000001" "1000003" ...
 $ Lat_Mean      : num  38.9 39 38.8 38.8 38.8 ...
 $ Lat_Med       : num  38.9 39 38.8 38.8 38.8 ...
 $ Lng_Mean      : num  -77.1 -77.4 -77 -77 -77 ...
 $ Lng_Med       : num  -77.1 -77.4 -77 -77 -77 ...
 $ Lat_MeaLessMed: num  8.37e-05 0.00 -5.40e-06 2.15e-05 9.27e-06 ...
 $ Lng_MeaLessMed: num  -8.47e-05 0.00 1.07e-05 1.62e-04 -4.82e-07 ...
 $ RowNum        : int  1 2 3 4 5 6 7 8 9 10 ...
summary(LL_Stats)
 StopID_Clean          Lat_Mean        Lat_Med         Lng_Mean     
 Length:10588       Min.   :38.09   Min.   :38.60   Min.   :-77.45  
 Class :character   1st Qu.:38.85   1st Qu.:38.85   1st Qu.:-77.10  
 Mode  :character   Median :38.90   Median :38.90   Median :-77.01  
                    Mean   :38.91   Mean   :38.91   Mean   :-77.03  
                    3rd Qu.:38.96   3rd Qu.:38.96   3rd Qu.:-76.95  
                    Max.   :39.19   Max.   :39.19   Max.   :-75.42  
    Lng_Med       Lat_MeaLessMed       Lng_MeaLessMed           RowNum     
 Min.   :-77.45   Min.   :-0.8069882   Min.   :-0.0102476   Min.   :    1  
 1st Qu.:-77.10   1st Qu.:-0.0000126   1st Qu.:-0.0000167   1st Qu.: 2648  
 Median :-77.01   Median : 0.0000003   Median :-0.0000003   Median : 5294  
 Mean   :-77.03   Mean   :-0.0001867   Mean   : 0.0003654   Mean   : 5294  
 3rd Qu.:-76.95   3rd Qu.: 0.0000141   3rd Qu.: 0.0000148   3rd Qu.: 7941  
 Max.   :-76.67   Max.   : 0.0093569   Max.   : 1.6050662   Max.   :10588  
View(head(arrange(LL_Stats,
                  Lat_MeaLessMed
                 ),
          500
         )
    )
View(head(arrange(LL_Stats,
                  desc(Lat_MeaLessMed)
                 ),
          500
         )
    )
View(head(arrange(LL_Stats,
                  Lng_MeaLessMed
                 ),
          500
         )
    )
  head(arrange(LL_Stats,
                  desc(Lng_MeaLessMed)
                 ),
          500
         )

Pulling in Zip Code data from api.geonames.org.

Need to group in bunches as http://api.geonames.org limits pulls to ~2000 per hour.

# URL EXAMPLE:
# http://api.geonames.org/findNearbyPostalCodesJSON?lat=38.89560&lng=-76.94873&radius=0&username=supermdat
url_1 <- "http://api.geonames.org/findNearbyPostalCodesJSON?lat="
url_2 <- "&lng="
url_3 <- "&radius=0&username="
username <- "supermdat"
# need to group in bunches as http://api.geonames.org limits pulls to ~2000 per hour
for(k in 0:10){
##### Store everything in multiple lists
pages1 <- list()
# system.time(
for(i in 1:10){
  lat <- filter(LL_Stats,
                RowNum == i
               ) %>%
    select(Lat_Med)
  
  lng <- filter(LL_Stats,
                RowNum == i
               ) %>%
    select(Lng_Med)
  
  APIData1 <- fromJSON(paste0(url_1,
                              lat,
                              url_2,
                              lng,
                              url_3,
                              username
                             ),
                       flatten = TRUE
                      )
  
  message("Retrieving Zip Code ", k, "_", i)
  
  pages1[[i]] <- APIData1$postalCodes
  
}
# )
##### Combine the lists into one page
assign(paste0("Zips", k),
       rbind.pages(pages1[sapply(pages1, length) > 0])
      )
Sys.sleep(4)
}
##### Combine all pages
Zips_All <- bind_rows(Zips0,
                      Zips1,
                      Zips2,
                      Zips3,
                      Zips4,
                      Zips5,
                      Zips6,
                      Zips7,
                      Zips8,
                      Zips9,
                      Zips10,
                      .id = "id"
                     ) %>% 
  mutate(UniqueLatLng = paste(lat, lng, sep = "__")
        )
# str(Zips_All)
# View(head(Zips_All))
# saveRDS(Zips_All, "Zips_All")

Reading in the saved Zips_All file. This is only done when re-running the code to avoid the delay in getting data from http://api.geonames.org

Zips_All <- readRDS("Zips_All")
str(Zips_All)
'data.frame':   10586 obs. of  12 variables:
 $ id          : chr  "1" "1" "1" "1" ...
 $ adminCode2  : chr  "013" "107" "001" "001" ...
 $ adminCode1  : chr  "VA" "VA" "DC" "DC" ...
 $ adminName2  : chr  "Arlington" "Loudoun" "District of Columbia" "District of Columbia" ...
 $ lng         : num  -77.1 -77.4 -77 -77 -77 ...
 $ distance    : chr  "0" "0" "0" "0" ...
 $ countryCode : chr  "US" "US" "US" "US" ...
 $ postalCode  : chr  "22202" "20166" "20032" "20032" ...
 $ adminName1  : chr  "Virginia" "Virginia" "District of Columbia" "District of Columbia" ...
 $ placeName   : chr  "Arlington" "Sterling" "Washington" "Washington" ...
 $ lat         : num  38.9 39 38.8 38.8 38.8 ...
 $ UniqueLatLng: chr  "38.85738__-77.055138" "38.962704__-77.433685" "38.816044__-77.017685" "38.816292__-77.018036" ...

Pulling in Zip Code data from api.geonames.org.

Linking the Zip Code data to LL_Stats (the unique Stop_Id-LatLong data).

# str(LL_Stats)
LL_Stats_UnqLatLng <- mutate(LL_Stats,
                             UniqueLatLng = paste(Lat_Med, Lng_Med, sep = "__")
                            )
# str(LL_Stats_UnqLatLng)
# View(head(LL_Stats_UnqLatLng))
LL_StatsZips <- left_join(LL_Stats_UnqLatLng,
                          Zips_All,
                          by = c("UniqueLatLng" = "UniqueLatLng")
                         )
str(LL_StatsZips)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   10588 obs. of  20 variables:
 $ StopID_Clean  : chr  "1" "10" "1000001" "1000003" ...
 $ Lat_Mean      : num  38.9 39 38.8 38.8 38.8 ...
 $ Lat_Med       : num  38.9 39 38.8 38.8 38.8 ...
 $ Lng_Mean      : num  -77.1 -77.4 -77 -77 -77 ...
 $ Lng_Med       : num  -77.1 -77.4 -77 -77 -77 ...
 $ Lat_MeaLessMed: num  8.37e-05 0.00 -5.40e-06 2.15e-05 9.27e-06 ...
 $ Lng_MeaLessMed: num  -8.47e-05 0.00 1.07e-05 1.62e-04 -4.82e-07 ...
 $ RowNum        : int  1 2 3 4 5 6 7 8 9 10 ...
 $ UniqueLatLng  : chr  "38.85738__-77.055138" "38.962704__-77.433685" "38.816044__-77.017685" "38.816292__-77.018036" ...
 $ id            : chr  "1" "1" "1" "1" ...
 $ adminCode2    : chr  "013" "107" "001" "001" ...
 $ adminCode1    : chr  "VA" "VA" "DC" "DC" ...
 $ adminName2    : chr  "Arlington" "Loudoun" "District of Columbia" "District of Columbia" ...
 $ lng           : num  -77.1 -77.4 -77 -77 -77 ...
 $ distance      : chr  "0" "0" "0" "0" ...
 $ countryCode   : chr  "US" "US" "US" "US" ...
 $ postalCode    : chr  "22202" "20166" "20032" "20032" ...
 $ adminName1    : chr  "Virginia" "Virginia" "District of Columbia" "District of Columbia" ...
 $ placeName     : chr  "Arlington" "Sterling" "Washington" "Washington" ...
 $ lat           : num  38.9 39 38.8 38.8 38.8 ...
# View(head(LL_StatsZips))
# Not sure whey these couldn't be found (why they're NA)
View(filter(LL_StatsZips,
            is.na(postalCode)
           )
    )

Join to create one dataset that also includes Zip variables.

rm(url_1, url_2, url_3, username, pages0, pages1, pages2, pages3, pages4, pages5, pages6, pages7, pages8, pages9, pages10, i, lat, lng, APIData0, APIData1, APIData2, APIData3, APIData4, APIData5, APIData6, APIData7, APIData8, APIData9, APIData10, LL_Stats, LL_Stats_UnqLatLng)
object 'pages0' not foundobject 'pages2' not foundobject 'pages3' not foundobject 'pages4' not foundobject 'pages5' not foundobject 'pages6' not foundobject 'pages7' not foundobject 'pages8' not foundobject 'pages9' not foundobject 'pages10' not foundobject 'APIData0' not foundobject 'APIData2' not foundobject 'APIData3' not foundobject 'APIData4' not foundobject 'APIData5' not foundobject 'APIData6' not foundobject 'APIData7' not foundobject 'APIData8' not foundobject 'APIData9' not foundobject 'APIData10' not found
AllDays_Zips <- left_join(AllDays_StopIDNew,
                          LL_StatsZips,
                          by = c("StopID_Clean" = "StopID_Clean")
                         ) %>% 
  rename(Stop_State = adminCode1,
         Stop_County = adminName2,
         Stop_City = placeName,
         Stop_Zip = postalCode
         )
rm(AllDays_StopIDNew, LL_StatsZips)
str(AllDays_Zips)
'data.frame':   3119443 obs. of  41 variables:
 $ group            : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 6 6 ...
 $ Route_Direction  : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence    : int  7 7 6 3 2 8 1 1 2 3 ...
 $ Stop_ID          : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ Event_Type       : int  4 5 4 4 4 3 3 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 2 3 3 3 1 1 3 3 3 ...
 $ Event_Time       : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:07:50" ...
 $ Departure_Time   : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:08:01" ...
 $ Dwell_Time       : int  0 11 0 0 0 0 104 0 0 0 ...
 $ Delta_Time       : int  -177 -27 24 165 25 73 719 0 74 76 ...
 $ Odometer_Distance: int  43543 43543 45139 46418 50115 51074 51303 53836 55633 56163 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 253 97 276 15 119 100 89 274 104 ...
 $ RowNum_OG        : int  1 2 3 4 5 6 7 8 9 10 ...
 $ StopID_New       : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean     : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ StopID_Indicator : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Lat_Mean         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lat_Med          : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lng_Mean         : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lng_Med          : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lat_MeaLessMed   : num  -0.000794 -0.000794 -0.000185 -0.000173 0.000162 ...
 $ Lng_MeaLessMed   : num  3.72e-04 3.72e-04 -6.78e-04 1.69e-04 4.11e-05 ...
 $ RowNum           : int  9715 9715 9716 9674 9673 8168 9701 9701 9829 9828 ...
 $ UniqueLatLng     : chr  "38.767807__-77.155136" "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" ...
 $ id               : chr  "10" "10" "10" "10" ...
 $ adminCode2       : chr  "059" "059" "059" "059" ...
 $ Stop_State       : chr  "VA" "VA" "VA" "VA" ...
 $ Stop_County      : chr  "Fairfax" "Fairfax" "Fairfax" "Fairfax" ...
 $ lng              : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ distance         : chr  "0" "0" "0" "0" ...
 $ countryCode      : chr  "US" "US" "US" "US" ...
 $ Stop_Zip         : chr  "22310" "22310" "22310" "22310" ...
 $ adminName1       : chr  "Virginia" "Virginia" "Virginia" "Virginia" ...
 $ Stop_City        : chr  "Alexandria" "Alexandria" "Alexandria" "Alexandria" ...
 $ lat              : num  38.8 38.8 38.8 38.8 38.8 ...

Updating variable types.

AllDays_Zips$Stop_State <- factor(AllDays_Zips$Stop_State)
AllDays_Zips$Stop_County <- factor(AllDays_Zips$Stop_County)
AllDays_Zips$Stop_Zip <- factor(AllDays_Zips$Stop_Zip)
AllDays_Zips$Stop_City <- factor(AllDays_Zips$Stop_City)
AllDays_Zips$distance <- as.numeric(AllDays_Zips$distance)
AllDays_Zips$countryCode <- factor(AllDays_Zips$countryCode)
AllDays_Zips$adminName1 <- factor(AllDays_Zips$adminName1)
str(AllDays_Zips)
'data.frame':   3119443 obs. of  41 variables:
 $ group            : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 6 6 ...
 $ Route_Direction  : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence    : int  7 7 6 3 2 8 1 1 2 3 ...
 $ Stop_ID          : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ Event_Type       : int  4 5 4 4 4 3 3 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 2 3 3 3 1 1 3 3 3 ...
 $ Event_Time       : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:07:50" ...
 $ Departure_Time   : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:08:01" ...
 $ Dwell_Time       : int  0 11 0 0 0 0 104 0 0 0 ...
 $ Delta_Time       : int  -177 -27 24 165 25 73 719 0 74 76 ...
 $ Odometer_Distance: int  43543 43543 45139 46418 50115 51074 51303 53836 55633 56163 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 253 97 276 15 119 100 89 274 104 ...
 $ RowNum_OG        : int  1 2 3 4 5 6 7 8 9 10 ...
 $ StopID_New       : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean     : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ StopID_Indicator : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Lat_Mean         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lat_Med          : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lng_Mean         : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lng_Med          : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lat_MeaLessMed   : num  -0.000794 -0.000794 -0.000185 -0.000173 0.000162 ...
 $ Lng_MeaLessMed   : num  3.72e-04 3.72e-04 -6.78e-04 1.69e-04 4.11e-05 ...
 $ RowNum           : int  9715 9715 9716 9674 9673 8168 9701 9701 9829 9828 ...
 $ UniqueLatLng     : chr  "38.767807__-77.155136" "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" ...
 $ id               : chr  "10" "10" "10" "10" ...
 $ adminCode2       : chr  "059" "059" "059" "059" ...
 $ Stop_State       : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County      : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ lng              : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ distance         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ countryCode      : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Zip         : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 150 123 123 123 123 123 ...
 $ adminName1       : Factor w/ 3 levels "District of Columbia",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_City        : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 2 49 49 49 49 49 ...
 $ lat              : num  38.8 38.8 38.8 38.8 38.8 ...

Feature engineering.

Inspecting incidences of consecutive Stop_IDs. This is done because investigation showed that many conseutive events occurr at the same Stop_ID, but with various Dwell_Times, Odometer_Distances, etc. All of which affect calculations and analyses.

Create data on the runs (consecutive Stop_IDs).

StopID_Runs <- rle(AllDays_Zips$StopID_Clean)
StopID_Runs$ends <- cumsum(StopID_Runs$lengths)
StopID_Runs$starts <- ifelse(is.na(lag(StopID_Runs$ends)
                                  ),
                             1,
                             lag(StopID_Runs$ends) + 1
                            )
str(StopID_Runs)
List of 4
 $ lengths: int [1:2809529] 2 1 1 1 1 2 1 1 1 1 ...
 $ values : chr [1:2809529] "5004572" "5004573" "5002210" "5002209" ...
 $ ends   : int [1:2809529] 2 3 4 5 6 8 9 10 11 12 ...
 $ starts : num [1:2809529] 1 3 4 5 6 7 9 10 11 12 ...
 - attr(*, "class")= chr "rle"
# class(StopID_Runs)
# 
# StopID_Runs_df <- data.frame(unclass(StopID_Runs))
# str(StopID_Runs_df)
# class(StopID_Runs_df)
# rm(StopID_Runs_df)

Trying to link data on RunsGroups with the original data (AllDays_Sorted). The goal is to select only one record per RunsGroup - that being the record with the longest Dwell_Time.

I attempted this computation using both data.frames (dplyr) and data.tables (data.table). However, with 2,809,062 rows in one dataset and 3,119,443 rows in the other dataset, the current computation time is over 5 days…so I’m trying a different strategy to only select the first record in a run.


# Create a RunsGroup variable for each run
# StopID_Runs_df$RunsGroup <- paste0("g", seq(1:nrow(StopID_Runs_df)
#                                            )
#                                   )
# 
# str(StopID_Runs_df)
# head(StopID_Runs_df, 25)
# tail(StopID_Runs_df, 25)
# 
# StopID_Runs_df <- StopID_Runs_df %>% 
#   mutate(RowNum = row_number()
#         )
# 
# str(StopID_Runs_df)
# head(StopID_Runs_df, 25)
# tail(StopID_Runs_df, 25)
# 
# 
# # Converting to data.tables for, hopefully, improved performance (speed) in computation
# StopID_Runs_dt <- data.table(StopID_Runs_df)
# setkey(StopID_Runs_dt, RowNum)
# str(StopID_Runs_dt)
# 
# AllDays_Sorted_dt <- data.table(AllDays_Sorted)
# setkey(AllDays_Sorted_dt, RowNum_OG)
# str(AllDays_Sorted_dt)
# # rm(AllDays_Sorted_dt)
# 
# 
# # Actual loop to perform the computations and link to original data (AllDays_Sorted_dt)
# GroupData <- list()
# for(i in 1:nrow(StopID_Runs_dt)
#    ) {
#   assign(paste0("group_", i),
#            StopID_Runs_dt[RowNum == i, RunsGroup]
#           )
# 
#     #####  The code below is the same code as above, but done with dplyr  #####
# 
#     # assign(paste0("group_", i),
#   #        filter(StopID_Runs_df,
#   #               RowNum == i
#   #              ) %>% 
#   #          select(RunsGroup)
#   #       )
# 
#   assign(paste0("group_", i, "_start"),
#          StopID_Runs_dt[RowNum == i, starts]
#         )
# 
#   assign(paste0("group_", i, "_end"),
#          StopID_Runs_dt[RowNum == i, ends]
#         )
# 
#   assign(paste0("group_", i, "_rows"),
#          AllDays_Sorted_dt[RowNum_OG >= as.numeric(get(paste0("group_", i, "_start")
#                                                       )
#                                                   ) &
#                            RowNum_OG <= as.numeric(get(paste0("group_", i, "_end")
#                                                       )
#                                                   ),
#                            RunsGroup := as.character(get(paste0("group_", i)
#                                                         )
#                                                     )
#                           ]
# 
#     #####  The code below is the same as the code above, but done with dplyr  #####
# 
#          # filter(AllDays_Sorted,
#          #        between(RowNum_OG,
#          #                as.numeric(get(paste0("group_", i, "_start")
#          #                              )
#          #                          ),
#          #                as.numeric(get(paste0("group_", i, "_end")
#          #                              )
#          #                          )
#          #               )
#          #       ) %>% 
#          #   mutate(RunsGroup = as.character(get(paste0("group_", i)
#          #                                     )
#          #                                 )
#          #        )
#         )
# 
#   GroupData[[i]] <- get(paste0("group_", i, "_rows"))
# 
#   message("Processing Group ", i, " of 2,809,062")
# }
# 
# 
# GroupData_df <- rbind.fill(GroupData)
# str(GroupData_df)
# head(GroupData_df)
# tail(GroupData_df)
# # rm(GroupData_df)
# 
# 
# group_1
# group_1_start
# group_1_end
# group_1_rows
# group_2_rows
# group_3_rows
# group_50_rows
# str(group_50_rows)
# group_2809062_rows
# GroupData[[1]]
# GroupData[[50]]
# 
# 
# #####  Testing Area (Below)  #####
# #####  Testing Area (Below)  #####
# #####  Testing Area (Below)  #####
# 
# # head(StopID_Runs$starts, 20)
# # head(AllDays_NewOrder$Stop_ID, 20)
# # 
# # 
# # dat <- as.data.frame(c(1,1,7,7,7,9,6,8,2,2,2,1,1,1,1,1))
# # colnames(dat)[1] <- "dat"
# # r <- rle(dat$dat)
# # dat$run <- rep(r$lengths, r$lengths)
# # dat$runLag <- lag(dat$run)
# # dat$cond <- rep(r$values, r$lengths)
# # dat
# # View(dat)

When consecutive Stop_ID occurrs, only take the first occurrence. This is done because the computation time to select only the record with the longest Dwell_Time for each run was too long (over 5 days).

This is probably less than ideal with regards to Dwell_Time, but should not make much difference for calculations of travel time, speed, etc.

AllDays_FirstStopID <- AllDays_Zips[StopID_Runs$starts, ]
dim(AllDays_Zips)
[1] 3119443      41
dim(AllDays_FirstStopID)
[1] 2809529      41
nrow(AllDays_Zips) - nrow(AllDays_FirstStopID)
[1] 309914
rm(AllDays_Zips, StopID_Runs)
str(AllDays_FirstStopID)
'data.frame':   2809529 obs. of  41 variables:
 $ group            : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction  : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence    : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type       : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time       : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time   : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time       : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time       : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance: int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG        : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New       : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean     : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Lat_Mean         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lat_Med          : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lng_Mean         : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lng_Med          : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lat_MeaLessMed   : num  -7.94e-04 -1.85e-04 -1.73e-04 1.62e-04 4.75e-05 ...
 $ Lng_MeaLessMed   : num  3.72e-04 -6.78e-04 1.69e-04 4.11e-05 -1.52e-04 ...
 $ RowNum           : int  9715 9716 9674 9673 8168 9701 9829 9828 9667 9829 ...
 $ UniqueLatLng     : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ id               : chr  "10" "10" "10" "10" ...
 $ adminCode2       : chr  "059" "059" "059" "059" ...
 $ Stop_State       : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County      : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ lng              : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ distance         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ countryCode      : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Zip         : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ adminName1       : Factor w/ 3 levels "District of Columbia",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_City        : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ lat              : num  38.8 38.8 38.8 38.8 38.8 ...

Feature engineering.

Creating new variables.

AllDays_AddVars <- mutate(AllDays_FirstStopID,
                          Odometer_Distance_Mi = Odometer_Distance / 5280, #5,280 feet in 1 mile
                          Dwell_Time2 = as.numeric(Departure_Time - Event_Time),
                          Event_Time_Yr = as.integer(year(Event_Time)),
                          Event_Time_Mth = as.integer(month(Event_Time)),
                          Event_Time_Date = day(Event_Time),
                          Event_Time_Day = wday(Event_Time, label = TRUE),
                          Event_Time_Hr = hour(Event_Time),
                          Event_Time_Min = minute(Event_Time),
                          Event_Time_HrGroup = factor(ifelse(Event_Time_Hr < 3,
                                                             "Group0_2",
                                                      ifelse(Event_Time_Hr < 6,
                                                             "Group3_5",
                                                      ifelse(Event_Time_Hr < 9,
                                                             "Group6_8",
                                                      ifelse(Event_Time_Hr < 12,
                                                             "Group9_11",
                                                      ifelse(Event_Time_Hr < 15,
                                                             "Group12_14",
                                                      ifelse(Event_Time_Hr < 18,
                                                             "Group15_17",
                                                      ifelse(Event_Time_Hr < 21,
                                                             "Group18_20",
                                                      ifelse(Event_Time_Hr < 24,
                                                             "Group21_23"
                                                            )))))))),
                                                         levels = c("Group0_2",
                                                                    "Group3_5",
                                                                    "Group6_8",
                                                                    "Group9_11",
                                                                    "Group12_14",
                                                                    "Group15_17",
                                                                    "Group18_20",
                                                                    "Group21_23"
                                                                   ),
                                                         ordered = TRUE
                                                     )
                         )
rm(AllDays_FirstStopID)
str(AllDays_AddVars)
'data.frame':   2809529 obs. of  50 variables:
 $ group               : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID              : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route               : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt            : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction     : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence       : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID             : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc           : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type          : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description   : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time          : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time      : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time          : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time          : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance   : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude            : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude           : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading             : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG           : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New          : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean        : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator    : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Lat_Mean            : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lat_Med             : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lng_Mean            : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lng_Med             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lat_MeaLessMed      : num  -7.94e-04 -1.85e-04 -1.73e-04 1.62e-04 4.75e-05 ...
 $ Lng_MeaLessMed      : num  3.72e-04 -6.78e-04 1.69e-04 4.11e-05 -1.52e-04 ...
 $ RowNum              : int  9715 9716 9674 9673 8168 9701 9829 9828 9667 9829 ...
 $ UniqueLatLng        : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ id                  : chr  "10" "10" "10" "10" ...
 $ adminCode2          : chr  "059" "059" "059" "059" ...
 $ Stop_State          : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County         : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ lng                 : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ distance            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ countryCode         : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Zip            : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ adminName1          : Factor w/ 3 levels "District of Columbia",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_City           : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ lat                 : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Odometer_Distance_Mi: num  8.25 8.55 8.79 9.49 9.67 ...
 $ Dwell_Time2         : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Event_Time_Yr       : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth      : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date     : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day      : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr       : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_Min      : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time_HrGroup  : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...

Function for calculating the distance traveled based on the Haversine formula. Original code from: https://www.r-bloggers.com/great-circle-distance-calculations-in-r/


# Calculates the geodesic distance between two points specified by radian latitude/longitude using the Haversine formula (hf)
# gcd.hf <- function(long1, lat1, long2, lat2) {
#   R <- 6371 # Earth mean radius [km]
#   delta.long <- (long2 - long1)
#   delta.lat <- (lat2 - lat1)
#   a <- sin(delta.lat/2)^2 + cos(lat1) * cos(lat2) * sin(delta.long/2)^2
#   c <- 2 * asin(min(1,sqrt(a)))
#   d = R * c * 0.621371 # 1 km = 0.621371 miles
#   return(d) # Distance in miles
# }

Feature engineering.

Creating more variables. Creating a BusEvent row number for future identification purposes. Then, creating various variables to analyze distance traveled and speed.

AllDays_BusDay <- group_by(AllDays_AddVars,
                           Bus_ID,
                           Event_Time_Date
                          ) %>% 
  mutate(BusDay_EventNum = row_number(),  # used to identify Bus movements on a particular date
         
         Route_Lag1 = lag(Route),  # used in future analyses to identify Route changes
         RouteAlt_Lag1 = lag(RouteAlt),  # used in future analyses to identify RouteAlt (direction) changes
         
         Odometer_Distance_Lag1 = lag(Odometer_Distance),
         
         Latitude_L1 = lag(Latitude),
         Longitude_L1 = lag(Longitude),
         # Lat_Radian = Latitude*pi/180,
         # Long_Radian = Longitude*pi/180,
         # Lat_Radian_L1 = lag(Lat_Radian),
         # Long_Radian_L1 = lag(Long_Radian),
         
         # accounting for potential negative distances
         TravelDistance_Ft = ifelse(Odometer_Distance > Odometer_Distance_Lag1,
                                    Odometer_Distance - Odometer_Distance_Lag1,
                                    NA
                                   ),
         TravelDistance_Mi = TravelDistance_Ft / 5280, #5,280 feet in 1 mile
         
         # TravelDistance_Mi2 = gcd.hf(long1 = Long_Radian_L1,
         #                             lat1 = Lat_Radian_L1,
         #                             long2 = Long_Radian,
         #                             lat2 = Lat_Radian
         #                            ),
         
         TravelDistance_Mi_Hvrs = 
                              # ifelse((is.na(Longitude_L1) | is.na(Latitude_L1)
                              #        ),
                              #        NA,
                              distHaversine(cbind(Longitude_L1, Latitude_L1),
                                            cbind(Longitude, Latitude)
                                           ) * 0.000621371, # 0.000621371 miles = 1 meter
         
         # accounting for potential negative times
         TravelTime_Sec = as.numeric(ifelse(Event_Time > lag(Departure_Time),
                                            Event_Time - lag(Departure_Time),
                                            NA
                                           )
                                    ),
         TravelTime_Hr = TravelTime_Sec / 3600, # 3,600 seconds in 1 hour
         
         # accounting for potential negative or zero travel times
         SpeedAvg_Mph = ifelse(TravelTime_Hr > 0,
                               TravelDistance_Mi / TravelTime_Hr,
                               NA
                              ),
         
         Start_ID = lag(StopID_Clean),
         Start_Desc = lag(Stop_Desc),
         StartStop_ID = ifelse(is.na(Start_ID),
                               paste("NULL", StopID_Clean, sep = "--"),
                               paste(Start_ID, StopID_Clean, sep = "--")
                              )
        ) %>% 
  as.data.frame()
rm(AllDays_AddVars)
str(AllDays_BusDay)
'data.frame':   2809529 obs. of  65 variables:
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID               : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New            : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Lat_Mean              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lat_Med               : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lng_Mean              : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lng_Med               : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lat_MeaLessMed        : num  -7.94e-04 -1.85e-04 -1.73e-04 1.62e-04 4.75e-05 ...
 $ Lng_MeaLessMed        : num  3.72e-04 -6.78e-04 1.69e-04 4.11e-05 -1.52e-04 ...
 $ RowNum                : int  9715 9716 9674 9673 8168 9701 9829 9828 9667 9829 ...
 $ UniqueLatLng          : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ id                    : chr  "10" "10" "10" "10" ...
 $ adminCode2            : chr  "059" "059" "059" "059" ...
 $ Stop_State            : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County           : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ lng                   : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ distance              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ countryCode           : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Zip              : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ adminName1            : Factor w/ 3 levels "District of Columbia",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_City             : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ lat                   : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Route_Lag1            : chr  NA "S80" "S80" "S80" ...
 $ RouteAlt_Lag1         : Factor w/ 14 levels "1","10","11",..: NA 1 1 1 1 1 1 6 6 6 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Latitude_L1           : num  NA 38.8 38.8 38.8 38.8 ...
 $ Longitude_L1          : num  NA -77.2 -77.2 -77.2 -77.2 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph          : num  NA 6.05 23.57 100.83 3.44 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
# summary(AllDays_BusDay)
# View(tail(AllDays_BusDay, 50))

Inspecting for issues with StartStop_ID (where the value is either NA or contains NULL). They ONLY exist when BusDay_EventNum = 1 (which is by design). So everything looks OK.

Stats (quantiles) overall for TravelDistance_Mi.

Quantiles_dt <- AllDays_BusDay %>% 
  mutate(TD_Mi_q2 = quantile(x = TravelDistance_Mi, probs = 0.02, na.rm = TRUE),
         TD_Mi_q98 = quantile(x = TravelDistance_Mi, probs = 0.98, na.rm = TRUE),
         TT_Sec_q2 = quantile(x = TravelTime_Sec, probs = 0.02, na.rm = TRUE),
         TT_Sec_q98 = quantile(x = TravelTime_Sec, probs = 0.98, na.rm = TRUE),
         TT_Hr_q2 = quantile(x = TravelTime_Hr, probs = 0.02, na.rm = TRUE),
         TT_Hr_q98 = quantile(x = TravelTime_Hr, probs = 0.98, na.rm = TRUE)
        ) %>% 
  data.table()
Stats <- Quantiles_dt %>% 
  mutate(TD_Mi_Mean = mean(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_Mean_F = mean(TravelDistance_Mi[TD_Mi_q2 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_q98],
                             na.rm = TRUE
                            ),
         TD_Mi_Med = median(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_Med_F = median(TravelDistance_Mi[TD_Mi_q2 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_q98],
                              na.rm = TRUE
                             ),
         TD_Mi_Cnt = sum(!is.na(TravelDistance_Mi)
                        ),
         TD_Mi_Cnt_F = sum(!is.na(TravelDistance_Mi[TD_Mi_q2 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_q98]
                                 )
                          ),
            
         TT_Sec_Mean = mean(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_Mean_F = mean(TravelTime_Sec[TT_Sec_q2 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_q98],
                              na.rm = TRUE
                             ),
         TT_Sec_Med = median(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_Med_F = median(TravelTime_Sec[TT_Sec_q2 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_q98],
                               na.rm = TRUE
                              ),
         TT_Sec_Cnt = sum(!is.na(TravelTime_Sec)
                         ),
         TT_Sec_Cnt_F = sum(!is.na(TravelTime_Sec[TT_Sec_q2 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_q98]
                                   )
                           ),
         TT_Hr_Mean = mean(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_Mean_F = mean(TravelTime_Hr[TT_Hr_q2 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_q98],
                             na.rm = TRUE
                            ),
         TT_Hr_Med = median(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_Med_F = median(TravelTime_Hr[TT_Hr_q2 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_q98],
                              na.rm = TRUE
                             ),
         TT_Hr_Cnt = sum(!is.na(TravelTime_Hr)
                        ),
         TT_Hr_Cnt_F = sum(!is.na(TravelTime_Hr[TT_Hr_q2 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_q98]
                                 )
                          )
        ) %>% 
  data.frame()
rm(AllDays_BusDay)
rm(Quantiles_dt)
str(Stats)
'data.frame':   2809529 obs. of  89 variables:
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID               : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New            : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Lat_Mean              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lat_Med               : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lng_Mean              : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lng_Med               : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lat_MeaLessMed        : num  -7.94e-04 -1.85e-04 -1.73e-04 1.62e-04 4.75e-05 ...
 $ Lng_MeaLessMed        : num  3.72e-04 -6.78e-04 1.69e-04 4.11e-05 -1.52e-04 ...
 $ RowNum                : int  9715 9716 9674 9673 8168 9701 9829 9828 9667 9829 ...
 $ UniqueLatLng          : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ id                    : chr  "10" "10" "10" "10" ...
 $ adminCode2            : chr  "059" "059" "059" "059" ...
 $ Stop_State            : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County           : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ lng                   : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ distance              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ countryCode           : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Zip              : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ adminName1            : Factor w/ 3 levels "District of Columbia",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_City             : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ lat                   : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Route_Lag1            : chr  NA "S80" "S80" "S80" ...
 $ RouteAlt_Lag1         : Factor w/ 14 levels "1","10","11",..: NA 1 1 1 1 1 1 6 6 6 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Latitude_L1           : num  NA 38.8 38.8 38.8 38.8 ...
 $ Longitude_L1          : num  NA -77.2 -77.2 -77.2 -77.2 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph          : num  NA 6.05 23.57 100.83 3.44 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ TD_Mi_q2              : num  0.0521 0.0521 0.0521 0.0521 0.0521 ...
 $ TD_Mi_q98             : num  0.959 0.959 0.959 0.959 0.959 ...
 $ TT_Sec_q2             : num  10 10 10 10 10 10 10 10 10 10 ...
 $ TT_Sec_q98            : num  349 349 349 349 349 349 349 349 349 349 ...
 $ TT_Hr_q2              : num  0.00278 0.00278 0.00278 0.00278 0.00278 ...
 $ TT_Hr_q98             : num  0.0969 0.0969 0.0969 0.0969 0.0969 ...
 $ TD_Mi_Mean            : num  0.308 0.308 0.308 0.308 0.308 ...
 $ TD_Mi_Mean_F          : num  0.232 0.232 0.232 0.232 0.232 ...
 $ TD_Mi_Med             : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Med_F           : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Cnt             : int  2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 ...
 $ TD_Mi_Cnt_F           : int  2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 ...
 $ TT_Sec_Mean           : num  105 105 105 105 105 ...
 $ TT_Sec_Mean_F         : num  56.6 56.6 56.6 56.6 56.6 ...
 $ TT_Sec_Med            : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Med_F          : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Cnt            : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Sec_Cnt_F          : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TT_Hr_Mean            : num  0.0291 0.0291 0.0291 0.0291 0.0291 ...
 $ TT_Hr_Mean_F          : num  0.0157 0.0157 0.0157 0.0157 0.0157 ...
 $ TT_Hr_Med             : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_Med_F           : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_Cnt             : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Hr_Cnt_F           : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
# View(head(Stats, 50))

Stats for StartStop_ID.

Quantiles_SS_dt <- group_by(Stats,
                            StartStop_ID
                           ) %>% 
  mutate(TD_Mi_SS_q5 = quantile(x = TravelDistance_Mi, probs = 0.05, na.rm = TRUE),
         TD_Mi_SS_q95 = quantile(x = TravelDistance_Mi, probs = 0.95, na.rm = TRUE),
         TT_Sec_SS_q5 = quantile(x = TravelTime_Sec, probs = 0.05, na.rm = TRUE),
         TT_Sec_SS_q95 = quantile(x = TravelTime_Sec, probs = 0.95, na.rm = TRUE),
         TT_Hr_SS_q5 = quantile(x = TravelTime_Hr, probs = 0.05, na.rm = TRUE),
         TT_Hr_SS_q95 = quantile(x = TravelTime_Hr, probs = 0.95, na.rm = TRUE)
        ) %>% 
  data.table()
Stats_StSt <- group_by(Quantiles_SS_dt,
                       StartStop_ID
                      ) %>% 
  mutate(TD_Mi_SS_Mean = mean(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_SS_Mean_F = mean(TravelDistance_Mi[TD_Mi_SS_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SS_q95],
                                na.rm = TRUE
                               ),
         TD_Mi_SS_Med = median(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_SS_Med_F = median(TravelDistance_Mi[TD_Mi_SS_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SS_q95],
                                 na.rm = TRUE
                                ),
         TD_Mi_SS_Cnt = sum(!is.na(TravelDistance_Mi)
                           ),
         TD_Mi_SS_Cnt_F = sum(!is.na(TravelDistance_Mi[TD_Mi_SS_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SS_q95]
                                    )
                             ),
            
         TT_Sec_SS_Mean = mean(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_SS_Mean_F = mean(TravelTime_Sec[TT_Sec_SS_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SS_q95],
                                 na.rm = TRUE
                                ),
         TT_Sec_SS_Med = median(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_SS_Med_F = median(TravelTime_Sec[TT_Sec_SS_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SS_q95],
                                  na.rm = TRUE
                                 ),
         TT_Sec_SS_Cnt = sum(!is.na(TravelTime_Sec)),
         TT_Sec_SS_Cnt_F = sum(!is.na(TravelTime_Sec[TT_Sec_SS_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SS_q95]
                                     )
                              ),
         TT_Hr_SS_Mean = mean(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_SS_Mean_F = mean(TravelTime_Hr[TT_Hr_SS_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SS_q95],
                                na.rm = TRUE
                               ),
         TT_Hr_SS_Med = median(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_SS_Med_F = median(TravelTime_Hr[TT_Hr_SS_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SS_q95],
                                 na.rm = TRUE
                                ),
         TT_Hr_SS_Cnt = sum(!is.na(TravelTime_Hr)),
         TT_Hr_SS_Cnt_F = sum(!is.na(TravelTime_Hr[TT_Hr_SS_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SS_q95]
                                    )
                             )
        ) %>% 
  data.frame()
rm(Stats)
rm(Quantiles_SS_dt)
str(Stats_StSt)
'data.frame':   2809529 obs. of  113 variables:
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID               : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New            : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Lat_Mean              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lat_Med               : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lng_Mean              : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lng_Med               : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lat_MeaLessMed        : num  -7.94e-04 -1.85e-04 -1.73e-04 1.62e-04 4.75e-05 ...
 $ Lng_MeaLessMed        : num  3.72e-04 -6.78e-04 1.69e-04 4.11e-05 -1.52e-04 ...
 $ RowNum                : int  9715 9716 9674 9673 8168 9701 9829 9828 9667 9829 ...
 $ UniqueLatLng          : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ id                    : chr  "10" "10" "10" "10" ...
 $ adminCode2            : chr  "059" "059" "059" "059" ...
 $ Stop_State            : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County           : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ lng                   : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ distance              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ countryCode           : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Zip              : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ adminName1            : Factor w/ 3 levels "District of Columbia",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_City             : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ lat                   : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Route_Lag1            : chr  NA "S80" "S80" "S80" ...
 $ RouteAlt_Lag1         : Factor w/ 14 levels "1","10","11",..: NA 1 1 1 1 1 1 6 6 6 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Latitude_L1           : num  NA 38.8 38.8 38.8 38.8 ...
 $ Longitude_L1          : num  NA -77.2 -77.2 -77.2 -77.2 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph          : num  NA 6.05 23.57 100.83 3.44 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ TD_Mi_q2              : num  0.0521 0.0521 0.0521 0.0521 0.0521 ...
 $ TD_Mi_q98             : num  0.959 0.959 0.959 0.959 0.959 ...
 $ TT_Sec_q2             : num  10 10 10 10 10 10 10 10 10 10 ...
 $ TT_Sec_q98            : num  349 349 349 349 349 349 349 349 349 349 ...
 $ TT_Hr_q2              : num  0.00278 0.00278 0.00278 0.00278 0.00278 ...
 $ TT_Hr_q98             : num  0.0969 0.0969 0.0969 0.0969 0.0969 ...
 $ TD_Mi_Mean            : num  0.308 0.308 0.308 0.308 0.308 ...
 $ TD_Mi_Mean_F          : num  0.232 0.232 0.232 0.232 0.232 ...
 $ TD_Mi_Med             : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Med_F           : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Cnt             : int  2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 ...
 $ TD_Mi_Cnt_F           : int  2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 ...
 $ TT_Sec_Mean           : num  105 105 105 105 105 ...
 $ TT_Sec_Mean_F         : num  56.6 56.6 56.6 56.6 56.6 ...
 $ TT_Sec_Med            : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Med_F          : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Cnt            : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Sec_Cnt_F          : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TT_Hr_Mean            : num  0.0291 0.0291 0.0291 0.0291 0.0291 ...
 $ TT_Hr_Mean_F          : num  0.0157 0.0157 0.0157 0.0157 0.0157 ...
 $ TT_Hr_Med             : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_Med_F           : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_Cnt             : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Hr_Cnt_F           : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TD_Mi_SS_q5           : num  NA 0.0252 0.2422 0.7324 0.0794 ...
 $ TD_Mi_SS_q95          : num  NA 0.626 0.242 1.008 0.176 ...
 $ TT_Sec_SS_q5          : num  NA 11.9 37 30.5 172.9 ...
 $ TT_Sec_SS_q95         : num  NA 346.3 37 75.8 189.1 ...
 $ TT_Hr_SS_q5           : num  NA 0.00331 0.01028 0.00849 0.04803 ...
 $ TT_Hr_SS_q95          : num  NA 0.0962 0.0103 0.0211 0.0525 ...
 $ TD_Mi_SS_Mean         : num  NaN 0.437 0.242 0.908 0.128 ...
 $ TD_Mi_SS_Mean_F       : num  NaN 0.457 0.242 0.977 NaN ...
 $ TD_Mi_SS_Med          : num  NA 0.512 0.242 0.962 0.128 ...
 $ TD_Mi_SS_Med_F        : num  NA 0.512 0.242 1.008 NA ...
  [list output truncated]
# View(head(Stats_StSt, 50))

Stats for StartStop_ID with Event_Time_HrGroup.

Quantiles_SSHG_dt <- group_by(Stats_StSt,
                              StartStop_ID,
                              Event_Time_HrGroup
                             ) %>% 
  mutate(TD_Mi_SSHG_q5 = quantile(x = TravelDistance_Mi, probs = 0.05, na.rm = TRUE),
         TD_Mi_SSHG_q95 = quantile(x = TravelDistance_Mi, probs = 0.95, na.rm = TRUE),
         TT_Sec_SSHG_q5 = quantile(x = TravelTime_Sec, probs = 0.05, na.rm = TRUE),
         TT_Sec_SSHG_q95 = quantile(x = TravelTime_Sec, probs = 0.95, na.rm = TRUE),
         TT_Hr_SSHG_q5 = quantile(x = TravelTime_Hr, probs = 0.05, na.rm = TRUE),
         TT_Hr_SSHG_q95 = quantile(x = TravelTime_Hr, probs = 0.95, na.rm = TRUE)
        ) %>% 
  data.table()
Stats_StSt_HrGrp <- group_by(Quantiles_SSHG_dt,
                             StartStop_ID,
                             Event_Time_HrGroup
                            ) %>% 
  mutate(TD_Mi_SSHG_Mean = mean(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_SSHG_Mean_F = mean(TravelDistance_Mi[TD_Mi_SSHG_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SSHG_q95],
                                  na.rm = TRUE
                                 ),
         TD_Mi_SSHG_Med = median(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_SSHG_Med_F = median(TravelDistance_Mi[TD_Mi_SSHG_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SSHG_q95],
                                   na.rm = TRUE
                                  ),
         TD_Mi_SSHG_Cnt = sum(!is.na(TravelDistance_Mi)
                             ),
         TD_Mi_SSHG_Cnt_F = sum(!is.na(TravelDistance_Mi[TD_Mi_SSHG_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SSHG_q95]
                                      )
                               ),
            
         TT_Sec_SSHG_Mean = mean(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_SSHG_Mean_F = mean(TravelTime_Sec[TT_Sec_SSHG_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SSHG_q95],
                                   na.rm = TRUE
                                  ),
         TT_Sec_SSHG_Med = median(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_SSHG_Med_F = median(TravelTime_Sec[TT_Sec_SSHG_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SSHG_q95],
                                    na.rm = TRUE
                                   ),
         TT_Sec_SSHG_Cnt = sum(!is.na(TravelTime_Sec)),
         TT_Sec_SSHG_Cnt_F = sum(!is.na(TravelTime_Sec[TT_Sec_SSHG_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SSHG_q95]
                                       )
                                ),
         TT_Hr_SSHG_Mean = mean(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_SSHG_Mean_F = mean(TravelTime_Hr[TT_Hr_SSHG_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SSHG_q95],
                                  na.rm = TRUE
                                 ),
         TT_Hr_SSHG_Med = median(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_SSHG_Med_F = median(TravelTime_Hr[TT_Hr_SSHG_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SSHG_q95],
                                   na.rm = TRUE
                                  ),
         TT_Hr_SSHG_Cnt = sum(!is.na(TravelTime_Hr)),
         TT_Hr_SSHG_Cnt_F = sum(!is.na(TravelTime_Hr[TT_Hr_SSHG_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SSHG_q95]
                                      )
                               )
        ) %>% 
  data.frame()
rm(Stats_StSt)
rm(Quantiles_SSHG_dt)
str(Stats_StSt_HrGrp)
'data.frame':   2809529 obs. of  137 variables:
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID               : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New            : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Lat_Mean              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lat_Med               : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lng_Mean              : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lng_Med               : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lat_MeaLessMed        : num  -7.94e-04 -1.85e-04 -1.73e-04 1.62e-04 4.75e-05 ...
 $ Lng_MeaLessMed        : num  3.72e-04 -6.78e-04 1.69e-04 4.11e-05 -1.52e-04 ...
 $ RowNum                : int  9715 9716 9674 9673 8168 9701 9829 9828 9667 9829 ...
 $ UniqueLatLng          : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ id                    : chr  "10" "10" "10" "10" ...
 $ adminCode2            : chr  "059" "059" "059" "059" ...
 $ Stop_State            : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County           : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ lng                   : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ distance              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ countryCode           : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Zip              : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ adminName1            : Factor w/ 3 levels "District of Columbia",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_City             : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ lat                   : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Route_Lag1            : chr  NA "S80" "S80" "S80" ...
 $ RouteAlt_Lag1         : Factor w/ 14 levels "1","10","11",..: NA 1 1 1 1 1 1 6 6 6 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Latitude_L1           : num  NA 38.8 38.8 38.8 38.8 ...
 $ Longitude_L1          : num  NA -77.2 -77.2 -77.2 -77.2 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph          : num  NA 6.05 23.57 100.83 3.44 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ TD_Mi_q2              : num  0.0521 0.0521 0.0521 0.0521 0.0521 ...
 $ TD_Mi_q98             : num  0.959 0.959 0.959 0.959 0.959 ...
 $ TT_Sec_q2             : num  10 10 10 10 10 10 10 10 10 10 ...
 $ TT_Sec_q98            : num  349 349 349 349 349 349 349 349 349 349 ...
 $ TT_Hr_q2              : num  0.00278 0.00278 0.00278 0.00278 0.00278 ...
 $ TT_Hr_q98             : num  0.0969 0.0969 0.0969 0.0969 0.0969 ...
 $ TD_Mi_Mean            : num  0.308 0.308 0.308 0.308 0.308 ...
 $ TD_Mi_Mean_F          : num  0.232 0.232 0.232 0.232 0.232 ...
 $ TD_Mi_Med             : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Med_F           : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Cnt             : int  2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 ...
 $ TD_Mi_Cnt_F           : int  2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 ...
 $ TT_Sec_Mean           : num  105 105 105 105 105 ...
 $ TT_Sec_Mean_F         : num  56.6 56.6 56.6 56.6 56.6 ...
 $ TT_Sec_Med            : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Med_F          : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Cnt            : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Sec_Cnt_F          : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TT_Hr_Mean            : num  0.0291 0.0291 0.0291 0.0291 0.0291 ...
 $ TT_Hr_Mean_F          : num  0.0157 0.0157 0.0157 0.0157 0.0157 ...
 $ TT_Hr_Med             : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_Med_F           : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_Cnt             : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Hr_Cnt_F           : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TD_Mi_SS_q5           : num  NA 0.0252 0.2422 0.7324 0.0794 ...
 $ TD_Mi_SS_q95          : num  NA 0.626 0.242 1.008 0.176 ...
 $ TT_Sec_SS_q5          : num  NA 11.9 37 30.5 172.9 ...
 $ TT_Sec_SS_q95         : num  NA 346.3 37 75.8 189.1 ...
 $ TT_Hr_SS_q5           : num  NA 0.00331 0.01028 0.00849 0.04803 ...
 $ TT_Hr_SS_q95          : num  NA 0.0962 0.0103 0.0211 0.0525 ...
 $ TD_Mi_SS_Mean         : num  NaN 0.437 0.242 0.908 0.128 ...
 $ TD_Mi_SS_Mean_F       : num  NaN 0.457 0.242 0.977 NaN ...
 $ TD_Mi_SS_Med          : num  NA 0.512 0.242 0.962 0.128 ...
 $ TD_Mi_SS_Med_F        : num  NA 0.512 0.242 1.008 NA ...
  [list output truncated]
# View(head(Stats_StSt_HrGrp, 50))

Feature engineering.

Calculating a variable to know if the RouteAlt changed. Could be useful in helping identifying weirdness in calculated distances and speeds.

str(AllDays_DirChange)
'data.frame':   2809529 obs. of  141 variables:
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID               : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New            : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Lat_Mean              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lat_Med               : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Lng_Mean              : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lng_Med               : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Lat_MeaLessMed        : num  -7.94e-04 -1.85e-04 -1.73e-04 1.62e-04 4.75e-05 ...
 $ Lng_MeaLessMed        : num  3.72e-04 -6.78e-04 1.69e-04 4.11e-05 -1.52e-04 ...
 $ RowNum                : int  9715 9716 9674 9673 8168 9701 9829 9828 9667 9829 ...
 $ UniqueLatLng          : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ id                    : chr  "10" "10" "10" "10" ...
 $ adminCode2            : chr  "059" "059" "059" "059" ...
 $ Stop_State            : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County           : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ lng                   : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ distance              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ countryCode           : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Zip              : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ adminName1            : Factor w/ 3 levels "District of Columbia",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_City             : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ lat                   : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Route_Lag1            : chr  NA "S80" "S80" "S80" ...
 $ RouteAlt_Lag1         : Factor w/ 14 levels "1","10","11",..: NA 1 1 1 1 1 1 6 6 6 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Latitude_L1           : num  NA 38.8 38.8 38.8 38.8 ...
 $ Longitude_L1          : num  NA -77.2 -77.2 -77.2 -77.2 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph          : num  NA 6.05 23.57 100.83 3.44 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ TD_Mi_q2              : num  0.0521 0.0521 0.0521 0.0521 0.0521 ...
 $ TD_Mi_q98             : num  0.959 0.959 0.959 0.959 0.959 ...
 $ TT_Sec_q2             : num  10 10 10 10 10 10 10 10 10 10 ...
 $ TT_Sec_q98            : num  349 349 349 349 349 349 349 349 349 349 ...
 $ TT_Hr_q2              : num  0.00278 0.00278 0.00278 0.00278 0.00278 ...
 $ TT_Hr_q98             : num  0.0969 0.0969 0.0969 0.0969 0.0969 ...
 $ TD_Mi_Mean            : num  0.308 0.308 0.308 0.308 0.308 ...
 $ TD_Mi_Mean_F          : num  0.232 0.232 0.232 0.232 0.232 ...
 $ TD_Mi_Med             : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Med_F           : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Cnt             : int  2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 ...
 $ TD_Mi_Cnt_F           : int  2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 ...
 $ TT_Sec_Mean           : num  105 105 105 105 105 ...
 $ TT_Sec_Mean_F         : num  56.6 56.6 56.6 56.6 56.6 ...
 $ TT_Sec_Med            : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Med_F          : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Cnt            : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Sec_Cnt_F          : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TT_Hr_Mean            : num  0.0291 0.0291 0.0291 0.0291 0.0291 ...
 $ TT_Hr_Mean_F          : num  0.0157 0.0157 0.0157 0.0157 0.0157 ...
 $ TT_Hr_Med             : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_Med_F           : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_Cnt             : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Hr_Cnt_F           : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TD_Mi_SS_q5           : num  NA 0.0252 0.2422 0.7324 0.0794 ...
 $ TD_Mi_SS_q95          : num  NA 0.626 0.242 1.008 0.176 ...
 $ TT_Sec_SS_q5          : num  NA 11.9 37 30.5 172.9 ...
 $ TT_Sec_SS_q95         : num  NA 346.3 37 75.8 189.1 ...
 $ TT_Hr_SS_q5           : num  NA 0.00331 0.01028 0.00849 0.04803 ...
 $ TT_Hr_SS_q95          : num  NA 0.0962 0.0103 0.0211 0.0525 ...
 $ TD_Mi_SS_Mean         : num  NaN 0.437 0.242 0.908 0.128 ...
 $ TD_Mi_SS_Mean_F       : num  NaN 0.457 0.242 0.977 NaN ...
 $ TD_Mi_SS_Med          : num  NA 0.512 0.242 0.962 0.128 ...
 $ TD_Mi_SS_Med_F        : num  NA 0.512 0.242 1.008 NA ...
  [list output truncated]

Re-ordering the variables to ease with comprehension.

AllDays_NewOrder <-  select(AllDays_DirChange,
                            RowNum_OG,
                            UniqueLatLng,
                            group,
                            StartStop_ID,
                            BusDay_EventNum,
                            Bus_ID,
                            Route,
                            RteChange2,
                            RouteAlt,
                            # RouteAlt_Lag1,
                            DirChange2,
                            Route_Direction,
                            Stop_Sequence,
                            Start_ID,
                            Start_Desc,
                            # Stop_ID,
                            StopID_Clean,
                            StopID_Indicator,
                            Stop_Desc,
                            countryCode,
                            Stop_State,
                            Stop_County,
                            Stop_City,
                            Stop_Zip,
                            Event_Type,
                            Event_Description,
                            Event_Time_Yr,
                            Event_Time_Mth,
                            Event_Time_Date,
                            Event_Time_Day,
                            Event_Time_Hr,
                            Event_Time_HrGroup,
                            Event_Time_Min,
                            Event_Time,
                            Departure_Time,
                            Dwell_Time,
                            Dwell_Time2,
                            Delta_Time,
                            Latitude,
                            Longitude,
                            Heading,
                            Odometer_Distance,
                            Odometer_Distance_Lag1,
                            Odometer_Distance_Mi,
                            TravelDistance_Ft,
                            TravelDistance_Mi,
                            TravelDistance_Mi_Hvrs,
                            TD_Mi_q2,
                            TD_Mi_q98,
                            TD_Mi_SS_q5,
                            TD_Mi_SS_q95,
                            TD_Mi_SSHG_q5,
                            TD_Mi_SSHG_q95,
                            TD_Mi_Mean,
                            TD_Mi_Mean_F,
                            TD_Mi_SS_Mean,
                            TD_Mi_SS_Mean_F,
                            TD_Mi_SSHG_Mean,
                            TD_Mi_SSHG_Mean_F,
                            TD_Mi_Med,
                            TD_Mi_Med_F,
                            TD_Mi_SS_Med,
                            TD_Mi_SS_Med_F,
                            TD_Mi_SSHG_Med,
                            TD_Mi_SSHG_Med_F,
                            TD_Mi_Cnt,
                            TD_Mi_Cnt_F,
                            TD_Mi_SS_Cnt,
                            TD_Mi_SS_Cnt_F,
                            TD_Mi_SSHG_Cnt,
                            TD_Mi_SSHG_Cnt_F,
                            TravelTime_Sec,
                            TT_Sec_q2,
                            TT_Sec_q98,
                            TT_Sec_SS_q5,
                            TT_Sec_SS_q95,
                            TT_Sec_SSHG_q5,
                            TT_Sec_SSHG_q95,
                            TT_Sec_Mean,
                            TT_Sec_Mean_F,
                            TT_Sec_SS_Mean,
                            TT_Sec_SS_Mean_F,
                            TT_Sec_SSHG_Mean,
                            TT_Sec_SSHG_Mean_F,
                            TT_Sec_Med,
                            TT_Sec_Med_F,
                            TT_Sec_SS_Med,
                            TT_Sec_SS_Med_F,
                            TT_Sec_SSHG_Med,
                            TT_Sec_SSHG_Med_F,
                            TT_Sec_Cnt,
                            TT_Sec_Cnt_F,
                            TT_Sec_SS_Cnt,
                            TT_Sec_SS_Cnt_F,
                            TT_Sec_SSHG_Cnt,
                            TT_Sec_SSHG_Cnt_F,
                            TravelTime_Hr,
                            TT_Hr_q2,
                            TT_Hr_q98,
                            TT_Hr_SS_q5,
                            TT_Hr_SS_q95,
                            TT_Hr_SSHG_q5,
                            TT_Hr_SSHG_q95,
                            TT_Hr_Mean,
                            TT_Hr_Mean_F,
                            TT_Hr_SS_Mean,
                            TT_Hr_SS_Mean_F,
                            TT_Hr_SSHG_Mean,
                            TT_Hr_SSHG_Mean_F,
                            TT_Hr_Med,
                            TT_Hr_Med_F,
                            TT_Hr_SS_Med,
                            TT_Hr_SS_Med_F,
                            TT_Hr_SSHG_Med,
                            TT_Hr_SSHG_Med_F,
                            TT_Hr_Cnt,
                            TT_Hr_Cnt_F,
                            TT_Hr_SS_Cnt,
                            TT_Hr_SS_Cnt_F,
                            TT_Hr_SSHG_Cnt,
                            TT_Hr_SSHG_Cnt_F,
                            SpeedAvg_Mph
                           )
rm(AllDays_DirChange)
str(select(AllDays_NewOrder,
           -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )
'data.frame':   2809529 obs. of  48 variables:
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ UniqueLatLng          : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RteChange2            : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ DirChange2            : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 1 2 2 2 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ countryCode           : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State            : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County           : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_City             : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ Stop_Zip              : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph          : num  NA 6.05 23.57 100.83 3.44 ...
str(AllDays_NewOrder)
'data.frame':   2809529 obs. of  120 variables:
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ UniqueLatLng          : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RteChange2            : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ DirChange2            : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 1 2 2 2 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ countryCode           : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State            : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County           : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_City             : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ Stop_Zip              : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TD_Mi_q2              : num  0.0521 0.0521 0.0521 0.0521 0.0521 ...
 $ TD_Mi_q98             : num  0.959 0.959 0.959 0.959 0.959 ...
 $ TD_Mi_SS_q5           : num  NA 0.0252 0.2422 0.7324 0.0794 ...
 $ TD_Mi_SS_q95          : num  NA 0.626 0.242 1.008 0.176 ...
 $ TD_Mi_SSHG_q5         : num  NA 0.0996 0.2422 0.7002 0.1816 ...
 $ TD_Mi_SSHG_q95        : num  NA 0.627 0.242 0.7 0.182 ...
 $ TD_Mi_Mean            : num  0.308 0.308 0.308 0.308 0.308 ...
 $ TD_Mi_Mean_F          : num  0.232 0.232 0.232 0.232 0.232 ...
 $ TD_Mi_SS_Mean         : num  NaN 0.437 0.242 0.908 0.128 ...
 $ TD_Mi_SS_Mean_F       : num  NaN 0.457 0.242 0.977 NaN ...
 $ TD_Mi_SSHG_Mean       : num  NaN 0.442 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Mean_F     : num  NaN 0.491 0.242 0.7 0.182 ...
 $ TD_Mi_Med             : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Med_F           : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_SS_Med          : num  NA 0.512 0.242 0.962 0.128 ...
 $ TD_Mi_SS_Med_F        : num  NA 0.512 0.242 1.008 NA ...
 $ TD_Mi_SSHG_Med        : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Med_F      : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_Cnt             : int  2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 ...
 $ TD_Mi_Cnt_F           : int  2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 ...
 $ TD_Mi_SS_Cnt          : int  0 14 1 4 2 87 22 118 91 11 ...
 $ TD_Mi_SS_Cnt_F        : int  0 12 1 3 0 77 18 106 81 9 ...
 $ TD_Mi_SSHG_Cnt        : int  0 7 1 1 1 23 6 29 28 3 ...
 $ TD_Mi_SSHG_Cnt_F      : int  0 5 1 1 1 19 4 25 24 1 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TT_Sec_q2             : num  10 10 10 10 10 10 10 10 10 10 ...
 $ TT_Sec_q98            : num  349 349 349 349 349 349 349 349 349 349 ...
 $ TT_Sec_SS_q5          : num  NA 11.9 37 30.5 172.9 ...
 $ TT_Sec_SS_q95         : num  NA 346.3 37 75.8 189.1 ...
 $ TT_Sec_SSHG_q5        : num  NA 59.6 37 25 190 11.6 236 51.5 55 8.8 ...
 $ TT_Sec_SSHG_q95       : num  NA 276 37 25 190 ...
 $ TT_Sec_Mean           : num  105 105 105 105 105 ...
 $ TT_Sec_Mean_F         : num  56.6 56.6 56.6 56.6 56.6 ...
 $ TT_Sec_SS_Mean        : num  NaN 215.8 37 58.2 181 ...
 $ TT_Sec_SS_Mean_F      : num  NaN 218.9 37 65.5 NaN ...
 $ TT_Sec_SSHG_Mean      : num  NaN 202 37 25 190 ...
 $ TT_Sec_SSHG_Mean_F    : num  NaN 226 37 25 190 ...
 $ TT_Sec_Med            : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Med_F          : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_SS_Med         : num  NA 223.5 37 65.5 181 ...
 $ TT_Sec_SS_Med_F       : num  NA 223.5 37 65.5 NA ...
 $ TT_Sec_SSHG_Med       : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_SSHG_Med_F     : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_Cnt            : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Sec_Cnt_F          : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TT_Sec_SS_Cnt         : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Sec_SS_Cnt_F       : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TT_Sec_SSHG_Cnt       : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Sec_SSHG_Cnt_F     : int  0 5 1 1 1 31 4 32 32 1 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ TT_Hr_q2              : num  0.00278 0.00278 0.00278 0.00278 0.00278 ...
 $ TT_Hr_q98             : num  0.0969 0.0969 0.0969 0.0969 0.0969 ...
 $ TT_Hr_SS_q5           : num  NA 0.00331 0.01028 0.00849 0.04803 ...
 $ TT_Hr_SS_q95          : num  NA 0.0962 0.0103 0.0211 0.0525 ...
  [list output truncated]
# View(head(AllDays_NewOrder, 500))
# View(tail(AllDays_NewOrder, 500))

Summarizing the data to help spot anomolies.

# View(
  group_by(AllDays_NewOrder,
              Stop_City) %>% 
       summarise(Cnt_Num = n(),
                 Cnt_Pct = 100*Cnt_Num / (nrow(AllDays_NewOrder)
                                         )
                ) %>% 
       arrange(desc(Cnt_Num))
# )
summary(AllDays_NewOrder)
   RowNum_OG       UniqueLatLng       group      StartStop_ID      
 Min.   :      1   Length:2809529     1:559521   Length:2809529    
 1st Qu.: 784722   Class :character   2:561389   Class :character  
 Median :1563300   Mode  :character   3:567794   Mode  :character  
 Mean   :1562504                      4:559180                     
 3rd Qu.:2337981                      5:561645                     
 Max.   :3119443                                                   
                                                                   
 BusDay_EventNum      Bus_ID        Route            RteChange2     
 Min.   :   1.0   Min.   :  11   Length:2809529     Change:  23772  
 1st Qu.: 113.0   1st Qu.:2922   Class :character   Same  :2785757  
 Median : 248.0   Median :6195   Mode  :character                   
 Mean   : 290.5   Mean   :5382                                      
 3rd Qu.: 428.0   3rd Qu.:7104                                      
 Max.   :1344.0   Max.   :8105                                      
                                                                    
    RouteAlt        DirChange2      Route_Direction   Stop_Sequence   
 2      :1128810   Change:  65126   SOUTH   :739235   Min.   :  1.00  
 1      :1065425   Same  :2744403   NORTH   :735203   1st Qu.: 12.00  
 3      : 260372                    WEST    :649706   Median : 24.00  
 4      : 130801                    EAST    :628074   Mean   : 26.83  
 5      :  75039                    LOOP    : 35611   3rd Qu.: 39.00  
 6      :  56408                    CLOCKWIS: 10671   Max.   :104.00  
 (Other):  92674                    (Other) : 11029                   
   Start_ID          Start_Desc        StopID_Clean       StopID_Indicator
 Length:2809529     Length:2809529     Length:2809529     ID_Bad:  18948  
 Class :character   Class :character   Class :character   ID_OK :2790581  
 Mode  :character   Mode  :character   Mode  :character                   
                                                                          
                                                                          
                                                                          
                                                                          
  Stop_Desc         countryCode    Stop_State                   Stop_County     
 Length:2809529     US  :2808431   DC  :1297006   District of Columbia:1297006  
 Class :character   NA's:   1098   MD  : 982401   Prince George's     : 589193  
 Mode  :character                  VA  : 529024   Montgomery          : 391422  
                                   NA's:   1098   Fairfax             : 204558  
                                                  Arlington           : 198618  
                                                  (Other)             : 127634  
                                                  NA's                :   1098  
         Stop_City          Stop_Zip         Event_Type 
 Washington   :1296626   20020  : 156333   Min.   :3.0  
 Silver Spring: 227570   20032  : 117215   1st Qu.:3.0  
 Arlington    : 198360   20019  : 116560   Median :4.0  
 Hyattsville  : 166930   20011  : 114518   Mean   :3.6  
 Alexandria   : 103776   20002  : 101086   3rd Qu.:4.0  
 (Other)      : 815169   (Other):2202719   Max.   :5.0  
 NA's         :   1098   NA's   :   1098                
                                          Event_Description   Event_Time_Yr 
 Serviced Stop                                     :1127366   Min.   :2016  
 Unknown Stop                                      :   2579   1st Qu.:2016  
 UnServiced Stop                                   :1679584   Median :2016  
                                                              Mean   :2016  
                                                              3rd Qu.:2016  
                                                              Max.   :2016  
                                                                            
 Event_Time_Mth Event_Time_Date Event_Time_Day Event_Time_Hr    Event_Time_HrGroup
 Min.   :10     Min.   :3.000   Sun  :     0   Min.   : 0.00   Group6_8  :611612  
 1st Qu.:10     1st Qu.:4.000   Mon  :559521   1st Qu.: 8.00   Group15_17:560103  
 Median :10     Median :5.000   Tues :561389   Median :13.00   Group18_20:461056  
 Mean   :10     Mean   :5.001   Wed  :567794   Mean   :12.97   Group9_11 :396514  
 3rd Qu.:10     3rd Qu.:6.000   Thurs:559180   3rd Qu.:18.00   Group12_14:353603  
 Max.   :10     Max.   :7.000   Fri  :561645   Max.   :23.00   Group21_23:244522  
                                Sat  :     0                   (Other)   :182119  
 Event_Time_Min    Event_Time                  Departure_Time               
 Min.   : 0.00   Min.   :2016-10-03 00:00:00   Min.   :2016-10-03 00:00:00  
 1st Qu.:14.00   1st Qu.:2016-10-04 08:36:14   1st Qu.:2016-10-04 08:36:20  
 Median :29.00   Median :2016-10-05 13:49:29   Median :2016-10-05 13:49:38  
 Mean   :29.43   Mean   :2016-10-05 13:29:21   Mean   :2016-10-05 13:29:28  
 3rd Qu.:44.00   3rd Qu.:2016-10-06 17:58:06   3rd Qu.:2016-10-06 17:58:13  
 Max.   :59.00   Max.   :2016-10-07 23:59:59   Max.   :2016-10-08 00:12:31  
                                                                            
   Dwell_Time       Dwell_Time2         Delta_Time         Latitude    
 Min.   :   0.00   Min.   :   0.000   Min.   :-5606.0   Min.   : 0.00  
 1st Qu.:   0.00   1st Qu.:   0.000   1st Qu.:   14.0   1st Qu.:38.86  
 Median :   0.00   Median :   0.000   Median :  157.0   Median :38.90  
 Mean   :  12.56   Mean   :   6.359   Mean   :  268.8   Mean   :38.91  
 3rd Qu.:   5.00   3rd Qu.:   4.000   3rd Qu.:  396.0   3rd Qu.:38.96  
 Max.   :6205.00   Max.   :6205.000   Max.   : 9426.0   Max.   :39.19  
                                                                       
   Longitude         Heading      Odometer_Distance  Odometer_Distance_Lag1
 Min.   :-77.45   Min.   :  0.0   Min.   :       0   Min.   :       0      
 1st Qu.:-77.07   1st Qu.: 89.0   1st Qu.:  177595   1st Qu.:  177326      
 Median :-77.02   Median :180.0   Median :  377510   Median :  376934      
 Mean   :-77.02   Mean   :176.9   Mean   :  426254   Mean   :  425713      
 3rd Qu.:-76.97   3rd Qu.:269.0   3rd Qu.:  623667   3rd Qu.:  622879      
 Max.   :  0.00   Max.   :360.0   Max.   :11108034   Max.   :10853226      
                                                     NA's   :6528          
 Odometer_Distance_Mi TravelDistance_Ft TravelDistance_Mi TravelDistance_Mi_Hvrs
 Min.   :   0.00      Min.   :      1   Min.   :  0.0     Min.   : 0.000        
 1st Qu.:  33.64      1st Qu.:    699   1st Qu.:  0.1     1st Qu.: 0.106        
 Median :  71.50      Median :   1044   Median :  0.2     Median : 0.142        
 Mean   :  80.73      Mean   :   1624   Mean   :  0.3     Mean   : 0.201        
 3rd Qu.: 118.12      3rd Qu.:   1518   3rd Qu.:  0.3     3rd Qu.: 0.193        
 Max.   :2103.79      Max.   :1323464   Max.   :250.7     Max.   :24.407        
                      NA's   :322734    NA's   :322734    NA's   :6528          
    TD_Mi_q2         TD_Mi_q98       TD_Mi_SS_q5       TD_Mi_SS_q95    
 Min.   :0.05208   Min.   :0.9585   Min.   :  0.000   Min.   :  0.000  
 1st Qu.:0.05208   1st Qu.:0.9585   1st Qu.:  0.086   1st Qu.:  0.262  
 Median :0.05208   Median :0.9585   Median :  0.104   Median :  0.326  
 Mean   :0.05208   Mean   :0.9585   Mean   :  0.164   Mean   :  0.488  
 3rd Qu.:0.05208   3rd Qu.:0.9585   3rd Qu.:  0.139   3rd Qu.:  0.436  
 Max.   :0.05208   Max.   :0.9585   Max.   :219.163   Max.   :246.949  
                                    NA's   :24757     NA's   :24757    
 TD_Mi_SSHG_q5    TD_Mi_SSHG_q95     TD_Mi_Mean      TD_Mi_Mean_F   
 Min.   :  0.00   Min.   :  0.00   Min.   :0.3076   Min.   :0.2318  
 1st Qu.:  0.09   1st Qu.:  0.25   1st Qu.:0.3076   1st Qu.:0.2318  
 Median :  0.11   Median :  0.31   Median :0.3076   Median :0.2318  
 Mean   :  0.18   Mean   :  0.47   Mean   :0.3076   Mean   :0.2318  
 3rd Qu.:  0.15   3rd Qu.:  0.42   3rd Qu.:0.3076   3rd Qu.:0.2318  
 Max.   :250.66   Max.   :250.66   Max.   :0.3076   Max.   :0.2318  
 NA's   :35629    NA's   :35629                                     
 TD_Mi_SS_Mean     TD_Mi_SS_Mean_F   TD_Mi_SSHG_Mean  TD_Mi_SSHG_Mean_F
 Min.   :  0.000   Min.   :  0.000   Min.   :  0.00   Min.   :  0.00   
 1st Qu.:  0.172   1st Qu.:  0.166   1st Qu.:  0.17   1st Qu.:  0.16   
 Median :  0.212   Median :  0.207   Median :  0.21   Median :  0.21   
 Mean   :  0.307   Mean   :  0.291   Mean   :  0.31   Mean   :  0.29   
 3rd Qu.:  0.267   3rd Qu.:  0.260   3rd Qu.:  0.27   3rd Qu.:  0.26   
 Max.   :219.163   Max.   :219.163   Max.   :250.66   Max.   :250.66   
 NA's   :24757     NA's   :27919     NA's   :35629    NA's   :44458    
   TD_Mi_Med       TD_Mi_Med_F      TD_Mi_SS_Med     TD_Mi_SS_Med_F   
 Min.   :0.1977   Min.   :0.1977   Min.   :  0.000   Min.   :  0.000  
 1st Qu.:0.1977   1st Qu.:0.1977   1st Qu.:  0.146   1st Qu.:  0.146  
 Median :0.1977   Median :0.1977   Median :  0.196   Median :  0.196  
 Mean   :0.1977   Mean   :0.1977   Mean   :  0.288   Mean   :  0.282  
 3rd Qu.:0.1977   3rd Qu.:0.1977   3rd Qu.:  0.265   3rd Qu.:  0.265  
 Max.   :0.1977   Max.   :0.1977   Max.   :219.163   Max.   :219.163  
                                   NA's   :24757     NA's   :27919    
 TD_Mi_SSHG_Med   TD_Mi_SSHG_Med_F   TD_Mi_Cnt        TD_Mi_Cnt_F     
 Min.   :  0.00   Min.   :  0.00   Min.   :2486795   Min.   :2387406  
 1st Qu.:  0.14   1st Qu.:  0.14   1st Qu.:2486795   1st Qu.:2387406  
 Median :  0.20   Median :  0.20   Median :2486795   Median :2387406  
 Mean   :  0.29   Mean   :  0.28   Mean   :2486795   Mean   :2387406  
 3rd Qu.:  0.27   3rd Qu.:  0.27   3rd Qu.:2486795   3rd Qu.:2387406  
 Max.   :250.66   Max.   :250.66   Max.   :2486795   Max.   :2387406  
 NA's   :35629    NA's   :44458                                       
  TD_Mi_SS_Cnt    TD_Mi_SS_Cnt_F   TD_Mi_SSHG_Cnt   TD_Mi_SSHG_Cnt_F
 Min.   :   0.0   Min.   :   0.0   Min.   :  0.00   Min.   :  0.00  
 1st Qu.: 163.0   1st Qu.: 146.0   1st Qu.: 26.00   1st Qu.: 22.00  
 Median : 280.0   Median : 252.0   Median : 45.00   Median : 39.00  
 Mean   : 347.4   Mean   : 312.7   Mean   : 57.27   Mean   : 50.85  
 3rd Qu.: 456.0   3rd Qu.: 411.0   3rd Qu.: 75.00   3rd Qu.: 67.00  
 Max.   :1543.0   Max.   :1388.0   Max.   :663.00   Max.   :595.00  
                                                                    
 TravelTime_Sec      TT_Sec_q2    TT_Sec_q98   TT_Sec_SS_q5      TT_Sec_SS_q95     
 Min.   :    1.0   Min.   :10   Min.   :349   Min.   :    1.00   Min.   :    1.00  
 1st Qu.:   25.0   1st Qu.:10   1st Qu.:349   1st Qu.:   15.00   1st Qu.:   48.00  
 Median :   39.0   Median :10   Median :349   Median :   22.00   Median :   80.05  
 Mean   :  104.9   Mean   :10   Mean   :349   Mean   :   61.26   Mean   :  183.28  
 3rd Qu.:   72.0   3rd Qu.:10   3rd Qu.:349   3rd Qu.:   34.00   3rd Qu.:  134.60  
 Max.   :60750.0   Max.   :10   Max.   :349   Max.   :60750.00   Max.   :60750.00  
 NA's   :6641                                 NA's   :6531       NA's   :6531      
 TT_Sec_SSHG_q5     TT_Sec_SSHG_q95     TT_Sec_Mean    TT_Sec_Mean_F  
 Min.   :    1.00   Min.   :    1.00   Min.   :104.9   Min.   :56.61  
 1st Qu.:   16.00   1st Qu.:   43.80   1st Qu.:104.9   1st Qu.:56.61  
 Median :   23.40   Median :   72.95   Median :104.9   Median :56.61  
 Mean   :   67.33   Mean   :  169.21   Mean   :104.9   Mean   :56.61  
 3rd Qu.:   36.70   3rd Qu.:  123.65   3rd Qu.:104.9   3rd Qu.:56.61  
 Max.   :60750.00   Max.   :60750.00   Max.   :104.9   Max.   :56.61  
 NA's   :6535       NA's   :6535                                      
 TT_Sec_SS_Mean     TT_Sec_SS_Mean_F   TT_Sec_SSHG_Mean   TT_Sec_SSHG_Mean_F
 Min.   :    1.00   Min.   :    1.00   Min.   :    1.00   Min.   :    1.00  
 1st Qu.:   29.06   1st Qu.:   27.54   1st Qu.:   28.38   1st Qu.:   27.21  
 Median :   44.16   Median :   41.91   Median :   43.38   Median :   41.48  
 Mean   :  104.88   Mean   :   91.34   Mean   :  104.88   Mean   :   93.53  
 3rd Qu.:   73.30   3rd Qu.:   69.25   3rd Qu.:   72.93   3rd Qu.:   70.12  
 Max.   :60750.00   Max.   :60750.00   Max.   :60750.00   Max.   :60750.00  
 NA's   :6531       NA's   :10519      NA's   :6535       NA's   :12811     
   TT_Sec_Med  TT_Sec_Med_F TT_Sec_SS_Med      TT_Sec_SS_Med_F   
 Min.   :39   Min.   :39    Min.   :    1.00   Min.   :    1.00  
 1st Qu.:39   1st Qu.:39    1st Qu.:   26.00   1st Qu.:   26.00  
 Median :39   Median :39    Median :   39.00   Median :   39.00  
 Mean   :39   Mean   :39    Mean   :   91.55   Mean   :   84.82  
 3rd Qu.:39   3rd Qu.:39    3rd Qu.:   65.00   3rd Qu.:   65.00  
 Max.   :39   Max.   :39    Max.   :60750.00   Max.   :60750.00  
                            NA's   :6531       NA's   :10519     
 TT_Sec_SSHG_Med    TT_Sec_SSHG_Med_F    TT_Sec_Cnt       TT_Sec_Cnt_F    
 Min.   :    1.00   Min.   :    1.00   Min.   :2802888   Min.   :2705189  
 1st Qu.:   26.00   1st Qu.:   26.00   1st Qu.:2802888   1st Qu.:2705189  
 Median :   39.00   Median :   38.50   Median :2802888   Median :2705189  
 Mean   :   94.94   Mean   :   88.44   Mean   :2802888   Mean   :2705189  
 3rd Qu.:   67.00   3rd Qu.:   66.50   3rd Qu.:2802888   3rd Qu.:2705189  
 Max.   :60750.00   Max.   :60750.00   Max.   :2802888   Max.   :2705189  
 NA's   :6535       NA's   :12811                                         
 TT_Sec_SS_Cnt    TT_Sec_SS_Cnt_F  TT_Sec_SSHG_Cnt  TT_Sec_SSHG_Cnt_F
 Min.   :   0.0   Min.   :   0.0   Min.   :  0.00   Min.   :  0.00   
 1st Qu.: 194.0   1st Qu.: 177.0   1st Qu.: 29.00   1st Qu.: 26.00   
 Median : 310.0   Median : 282.0   Median : 51.00   Median : 46.00   
 Mean   : 384.4   Mean   : 349.8   Mean   : 63.46   Mean   : 57.09   
 3rd Qu.: 497.0   3rd Qu.: 452.0   3rd Qu.: 83.00   3rd Qu.: 74.00   
 Max.   :1664.0   Max.   :1523.0   Max.   :691.00   Max.   :634.00   
                                                                     
 TravelTime_Hr       TT_Hr_q2          TT_Hr_q98        TT_Hr_SS_q5    
 Min.   : 0.000   Min.   :0.002778   Min.   :0.09694   Min.   : 0.000  
 1st Qu.: 0.007   1st Qu.:0.002778   1st Qu.:0.09694   1st Qu.: 0.004  
 Median : 0.011   Median :0.002778   Median :0.09694   Median : 0.006  
 Mean   : 0.029   Mean   :0.002778   Mean   :0.09694   Mean   : 0.017  
 3rd Qu.: 0.020   3rd Qu.:0.002778   3rd Qu.:0.09694   3rd Qu.: 0.009  
 Max.   :16.875   Max.   :0.002778   Max.   :0.09694   Max.   :16.875  
 NA's   :6641                                          NA's   :6531    
  TT_Hr_SS_q95    TT_Hr_SSHG_q5    TT_Hr_SSHG_q95     TT_Hr_Mean     
 Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.02913  
 1st Qu.: 0.013   1st Qu.: 0.004   1st Qu.: 0.012   1st Qu.:0.02913  
 Median : 0.022   Median : 0.006   Median : 0.020   Median :0.02913  
 Mean   : 0.051   Mean   : 0.019   Mean   : 0.047   Mean   :0.02913  
 3rd Qu.: 0.037   3rd Qu.: 0.010   3rd Qu.: 0.034   3rd Qu.:0.02913  
 Max.   :16.875   Max.   :16.875   Max.   :16.875   Max.   :0.02913  
 NA's   :6531     NA's   :6535     NA's   :6535                      
  TT_Hr_Mean_F     TT_Hr_SS_Mean    TT_Hr_SS_Mean_F  TT_Hr_SSHG_Mean 
 Min.   :0.01573   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
 1st Qu.:0.01573   1st Qu.: 0.008   1st Qu.: 0.008   1st Qu.: 0.008  
 Median :0.01573   Median : 0.012   Median : 0.012   Median : 0.012  
 Mean   :0.01573   Mean   : 0.029   Mean   : 0.025   Mean   : 0.029  
 3rd Qu.:0.01573   3rd Qu.: 0.020   3rd Qu.: 0.019   3rd Qu.: 0.020  
 Max.   :0.01573   Max.   :16.875   Max.   :16.875   Max.   :16.875  
                   NA's   :6531     NA's   :10532    NA's   :6535    
 TT_Hr_SSHG_Mean_F   TT_Hr_Med        TT_Hr_Med_F       TT_Hr_SS_Med   
 Min.   : 0.000    Min.   :0.01083   Min.   :0.01083   Min.   : 0.000  
 1st Qu.: 0.008    1st Qu.:0.01083   1st Qu.:0.01083   1st Qu.: 0.007  
 Median : 0.012    Median :0.01083   Median :0.01083   Median : 0.011  
 Mean   : 0.026    Mean   :0.01083   Mean   :0.01083   Mean   : 0.025  
 3rd Qu.: 0.019    3rd Qu.:0.01083   3rd Qu.:0.01083   3rd Qu.: 0.018  
 Max.   :16.875    Max.   :0.01083   Max.   :0.01083   Max.   :16.875  
 NA's   :12895                                         NA's   :6531    
 TT_Hr_SS_Med_F   TT_Hr_SSHG_Med   TT_Hr_SSHG_Med_F   TT_Hr_Cnt      
 Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :2802888  
 1st Qu.: 0.007   1st Qu.: 0.007   1st Qu.: 0.007   1st Qu.:2802888  
 Median : 0.011   Median : 0.011   Median : 0.011   Median :2802888  
 Mean   : 0.024   Mean   : 0.026   Mean   : 0.025   Mean   :2802888  
 3rd Qu.: 0.018   3rd Qu.: 0.019   3rd Qu.: 0.018   3rd Qu.:2802888  
 Max.   :16.875   Max.   :16.875   Max.   :16.875   Max.   :2802888  
 NA's   :10532    NA's   :6535     NA's   :12895                     
  TT_Hr_Cnt_F       TT_Hr_SS_Cnt    TT_Hr_SS_Cnt_F   TT_Hr_SSHG_Cnt  
 Min.   :2705189   Min.   :   0.0   Min.   :   0.0   Min.   :  0.00  
 1st Qu.:2705189   1st Qu.: 194.0   1st Qu.: 176.0   1st Qu.: 29.00  
 Median :2705189   Median : 310.0   Median : 282.0   Median : 51.00  
 Mean   :2705189   Mean   : 384.4   Mean   : 349.6   Mean   : 63.46  
 3rd Qu.:2705189   3rd Qu.: 497.0   3rd Qu.: 452.0   3rd Qu.: 83.00  
 Max.   :2705189   Max.   :1664.0   Max.   :1523.0   Max.   :691.00  
                                                                     
 TT_Hr_SSHG_Cnt_F  SpeedAvg_Mph    
 Min.   :  0.00   Min.   :    0.0  
 1st Qu.: 26.00   1st Qu.:   10.1  
 Median : 46.00   Median :   16.7  
 Mean   : 57.05   Mean   :   26.5  
 3rd Qu.: 74.00   3rd Qu.:   31.2  
 Max.   :634.00   Max.   :22924.1  
                  NA's   :322762   

Investigation of TravelDistance_Mi.

View(TravDistMi_Pctiles): 99% of TravelDistance_Mi are about 1 mile or less…but some weird TravelDistance_Mi values (e.g., 584 miles traveled) exist.

TravDistMi_Ntile <- as.data.frame(AllDays_NewOrder$TravelDistance_Mi) %>% 
  mutate(#Pctile = ntile(AllDays_NewOrder$TravelDistance_Mi, 100),
         #MinR = min_rank(AllDays_NewOrder$TravelDistance_Mi),
         PctR = percent_rank(AllDays_NewOrder$TravelDistance_Mi),
         PctR_Round = round(PctR, 2)
        ) 
colnames(TravDistMi_Ntile)[1] <- "TravelDistance_Mi"
# str(TravDistMi_Ntile)
TravDistMi_Ntile_Rows <- nrow(TravDistMi_Ntile)
# View(tail(TravDistMi_Ntile, 500))
TravDistMi_Pctiles <- group_by(TravDistMi_Ntile,
                               PctR_Round
                              ) %>% 
  summarise(
    MinTravDistMiAtPctile = min(TravelDistance_Mi),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / TravDistMi_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )
rm(TravDistMi_Ntile)
rm(TravDistMi_Ntile_Rows)
View(TravDistMi_Pctiles)
TravDistMi_Pctiles

Investigation of TravelDistance_Mi.

Why are some TravelDistance_Mi “NA”? It looks like partially because the records are the first trip of the day (for that bus), so I purposefully set the distance to “NA”. Another reason is due to the odometer recording a value less than the previous odometer recording. In most cases, I have no explanation for this - though I have observed about 67% of all instances where TravelDistance_Mi is NA (other than because it’s the first record of the day) are instances where DirChange2 is “Change”. This is weird and should be asked to WMATA.

Investigation of TravelDistance_Mi.

These records are NA becuase the current record odometer is less than the previous record odometer. Theoretically, this should NOT happen. Me: it appears that about 67% of all instances where TravelDistance_Mi is NA (other than because it’s the first record of the day) are instances where DirChange2 is “Change”. This is weird and should be asked to WMATA.

View(filter(AllDays_NewOrder,
            between(RowNum_OG, 194, 214) | # 204
              between(RowNum_OG, 440, 460) | # 450
              between(RowNum_OG, 478, 498) | # 488
              between(RowNum_OG, 510, 530) # 520
           )
    )
TestTable <- filter(AllDays_NewOrder,
                    BusDay_EventNum != 1
                   ) %>% 
  mutate(TravelDistance_NA = as.factor(ifelse(is.na(TravelDistance_Mi),
                                              "True",
                                              "False"
                                             )
                                      )
        ) %>%
  group_by(DirChange2, TravelDistance_NA) %>%
  summarise(TravDistMi_NACnts = n()
           )
# TestTable
TestTable_Spread <- as.data.frame(spread(TestTable,
                                         TravelDistance_NA,
                                         TravDistMi_NACnts
                                        )
                                 ) %>% 
  select(False,
         True
        )
row.names(TestTable_Spread) <- c("Change", "Same")
# str(TestTable_Spread)
# TestTable_Spread
prop.table(as.table(as.matrix(TestTable_Spread)
                   ),
           1
          )
           False      True
Change 0.8267006 0.1732994
Same   0.8884818 0.1115182
prop.table(as.table(as.matrix(TestTable_Spread)
                   ),
           2
          )
            False       True
Change 0.01948009 0.03211514
Same   0.98051991 0.96788486

Investigation of TravelDistance_Mi.

Let’s look at just the TravelDistance_Mi values that are NOT “NA”.

rm(TestTable, TestTable_Spread)
TravelDistance_Mi_NoNA <- filter(AllDays_NewOrder,
                                 # TravelDistance_Mi != 0 &
                                 !is.na(TravelDistance_Mi)
                                )
dim(AllDays_NewOrder)
[1] 2809529     120
dim(TravelDistance_Mi_NoNA)
[1] 2486795     120
nrow(AllDays_NewOrder) - nrow(TravelDistance_Mi_NoNA)
[1] 322734
str(TravelDistance_Mi_NoNA)
'data.frame':   2486795 obs. of  120 variables:
 $ RowNum_OG             : int  3 4 5 6 7 9 10 11 12 13 ...
 $ UniqueLatLng          : chr  "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" "38.766769__-77.169312" ...
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID          : chr  "5004572--5004573" "5004573--5002210" "5002210--5002209" "5002209--5000070" ...
 $ BusDay_EventNum       : int  2 3 4 5 6 7 8 9 10 11 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RteChange2            : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 6 6 6 6 6 ...
 $ DirChange2            : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 1 2 2 2 2 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  6 3 2 8 1 2 3 4 2 6 ...
 $ Start_ID              : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Start_Desc            : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ StopID_Clean          : chr  "5004573" "5002210" "5002209" "5000070" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_Desc             : chr  "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" "FRANCONIA-SPRGFLD STA. + BUS BAY D" ...
 $ countryCode           : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State            : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County           : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_City             : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 49 49 49 49 49 49 49 ...
 $ Stop_Zip              : Factor w/ 153 levels "20001","20002",..: 150 150 150 123 123 123 123 123 123 123 ...
 $ Event_Type            : int  4 4 4 3 3 4 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 1 1 3 3 3 3 3 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Min        : int  9 10 10 13 14 21 21 23 23 26 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:09:47" "2016-10-03 06:10:24" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:09:47" "2016-10-03 06:10:24" ...
 $ Dwell_Time            : int  0 0 0 0 104 0 0 0 0 0 ...
 $ Dwell_Time2           : num  0 0 0 0 104 0 0 0 0 0 ...
 $ Delta_Time            : int  24 165 25 73 719 74 76 63 69 165 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  97 276 15 119 100 274 104 241 274 1 ...
 $ Odometer_Distance     : int  45139 46418 50115 51074 51303 55633 56163 56285 57262 58363 ...
 $ Odometer_Distance_Lag1: int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Odometer_Distance_Mi  : num  8.55 8.79 9.49 9.67 9.72 ...
 $ TravelDistance_Ft     : int  1596 1279 3697 959 229 4330 530 122 977 1101 ...
 $ TravelDistance_Mi     : num  0.3023 0.2422 0.7002 0.1816 0.0434 ...
 $ TravelDistance_Mi_Hvrs: num  0.15 0.105 0.165 0.832 0.068 ...
 $ TD_Mi_q2              : num  0.0521 0.0521 0.0521 0.0521 0.0521 ...
 $ TD_Mi_q98             : num  0.959 0.959 0.959 0.959 0.959 ...
 $ TD_Mi_SS_q5           : num  0.025246 0.242235 0.732434 0.079432 0.000436 ...
 $ TD_Mi_SS_q95          : num  0.626 0.242 1.008 0.176 10.435 ...
 $ TD_Mi_SSHG_q5         : num  0.09956 0.24223 0.70019 0.18163 0.00269 ...
 $ TD_Mi_SSHG_q95        : num  0.627 0.242 0.7 0.182 0.497 ...
 $ TD_Mi_Mean            : num  0.308 0.308 0.308 0.308 0.308 ...
 $ TD_Mi_Mean_F          : num  0.232 0.232 0.232 0.232 0.232 ...
 $ TD_Mi_SS_Mean         : num  0.437 0.242 0.908 0.128 1.166 ...
 $ TD_Mi_SS_Mean_F       : num  0.457 0.242 0.977 NaN 0.226 ...
 $ TD_Mi_SSHG_Mean       : num  0.442 0.242 0.7 0.182 0.232 ...
 $ TD_Mi_SSHG_Mean_F     : num  0.491 0.242 0.7 0.182 0.228 ...
 $ TD_Mi_Med             : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Med_F           : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_SS_Med          : num  0.5116 0.2422 0.9616 0.1278 0.0426 ...
 $ TD_Mi_SS_Med_F        : num  0.5116 0.2422 1.0081 NA 0.0426 ...
 $ TD_Mi_SSHG_Med        : num  0.512 0.242 0.7 0.182 0.108 ...
 $ TD_Mi_SSHG_Med_F      : num  0.512 0.242 0.7 0.182 0.108 ...
 $ TD_Mi_Cnt             : int  2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 ...
 $ TD_Mi_Cnt_F           : int  2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 ...
 $ TD_Mi_SS_Cnt          : int  14 1 4 2 87 22 118 91 11 2 ...
 $ TD_Mi_SS_Cnt_F        : int  12 1 3 0 77 18 106 81 9 0 ...
 $ TD_Mi_SSHG_Cnt        : int  7 1 1 1 23 6 29 28 3 1 ...
 $ TD_Mi_SSHG_Cnt_F      : int  5 1 1 1 19 4 25 24 1 1 ...
 $ TravelTime_Sec        : num  180 37 25 190 29 288 52 76 8 189 ...
 $ TT_Sec_q2             : num  10 10 10 10 10 10 10 10 10 10 ...
 $ TT_Sec_q98            : num  349 349 349 349 349 349 349 349 349 349 ...
 $ TT_Sec_SS_q5          : num  11.9 37 30.5 172.9 10 ...
 $ TT_Sec_SS_q95         : num  346.3 37 75.8 189.1 1737.2 ...
 $ TT_Sec_SSHG_q5        : num  59.6 37 25 190 11.6 236 51.5 55 8.8 189 ...
 $ TT_Sec_SSHG_q95       : num  276 37 25 190 675 ...
 $ TT_Sec_Mean           : num  105 105 105 105 105 ...
 $ TT_Sec_Mean_F         : num  56.6 56.6 56.6 56.6 56.6 ...
 $ TT_Sec_SS_Mean        : num  215.8 37 58.2 181 585.3 ...
 $ TT_Sec_SS_Mean_F      : num  218.9 37 65.5 NaN 249.3 ...
 $ TT_Sec_SSHG_Mean      : num  202 37 25 190 257 ...
 $ TT_Sec_SSHG_Mean_F    : num  226 37 25 190 244 ...
 $ TT_Sec_Med            : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Med_F          : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_SS_Med         : num  223.5 37 65.5 181 33 ...
 $ TT_Sec_SS_Med_F       : num  223.5 37 65.5 NA 32 ...
 $ TT_Sec_SSHG_Med       : num  219 37 25 190 134 286 60 65 16 189 ...
 $ TT_Sec_SSHG_Med_F     : num  219 37 25 190 134 286 60 65 16 189 ...
 $ TT_Sec_Cnt            : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Sec_Cnt_F          : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TT_Sec_SS_Cnt         : int  14 1 4 2 173 22 141 141 11 2 ...
 $ TT_Sec_SS_Cnt_F       : int  12 1 2 0 156 18 127 128 9 0 ...
 $ TT_Sec_SSHG_Cnt       : int  7 1 1 1 35 6 36 35 3 1 ...
 $ TT_Sec_SSHG_Cnt_F     : int  5 1 1 1 31 4 32 32 1 1 ...
 $ TravelTime_Hr         : num  0.05 0.01028 0.00694 0.05278 0.00806 ...
 $ TT_Hr_q2              : num  0.00278 0.00278 0.00278 0.00278 0.00278 ...
 $ TT_Hr_q98             : num  0.0969 0.0969 0.0969 0.0969 0.0969 ...
 $ TT_Hr_SS_q5           : num  0.00331 0.01028 0.00849 0.04803 0.00278 ...
 $ TT_Hr_SS_q95          : num  0.0962 0.0103 0.0211 0.0525 0.4826 ...
  [list output truncated]
summary(TravelDistance_Mi_NoNA)
   RowNum_OG       UniqueLatLng       group      StartStop_ID      
 Min.   :      3   Length:2486795     1:496190   Length:2486795    
 1st Qu.: 786568   Class :character   2:497932   Class :character  
 Median :1590497   Mode  :character   3:501611   Mode  :character  
 Mean   :1578192                      4:495069                     
 3rd Qu.:2351264                      5:495993                     
 Max.   :3119443                                                   
                                                                   
 BusDay_EventNum      Bus_ID        Route            RteChange2     
 Min.   :   2.0   Min.   :  11   Length:2486795     Change:  13709  
 1st Qu.: 115.0   1st Qu.:2923   Class :character   Same  :2473086  
 Median : 251.0   Median :6202   Mode  :character                   
 Mean   : 293.2   Mean   :5431                                      
 3rd Qu.: 431.0   3rd Qu.:7113                                      
 Max.   :1344.0   Max.   :8105                                      
                                                                    
    RouteAlt       DirChange2      Route_Direction   Stop_Sequence   
 2      :994645   Change:  48443   SOUTH   :667198   Min.   :  1.00  
 1      :943279   Same  :2438352   NORTH   :662471   1st Qu.: 12.00  
 3      :229032                    WEST    :565616   Median : 24.00  
 4      :117090                    EAST    :543386   Mean   : 27.13  
 5      : 67811                    LOOP    : 33484   3rd Qu.: 39.00  
 6      : 51391                    CLOCKWIS:  7012   Max.   :104.00  
 (Other): 83547                    (Other) :  7628                   
   Start_ID          Start_Desc        StopID_Clean       StopID_Indicator
 Length:2486795     Length:2486795     Length:2486795     ID_Bad:  14271  
 Class :character   Class :character   Class :character   ID_OK :2472524  
 Mode  :character   Mode  :character   Mode  :character                   
                                                                          
                                                                          
                                                                          
                                                                          
  Stop_Desc         countryCode    Stop_State                   Stop_County     
 Length:2486795     US  :2485808   DC  :1148297   District of Columbia:1148297  
 Class :character   NA's:    987   MD  : 872720   Prince George's     : 525575  
 Mode  :character                  VA  : 464791   Montgomery          : 345558  
                                   NA's:    987   Fairfax             : 178174  
                                                  Arlington           : 176087  
                                                  (Other)             : 112117  
                                                  NA's                :    987  
         Stop_City          Stop_Zip         Event_Type   
 Washington   :1147938   20020  : 136778   Min.   :3.000  
 Silver Spring: 201381   20019  : 110150   1st Qu.:3.000  
 Arlington    : 175923   20032  : 106396   Median :4.000  
 Hyattsville  : 151249   20011  : 105580   Mean   :3.626  
 Alexandria   :  90916   20002  :  91962   3rd Qu.:4.000  
 (Other)      : 718401   (Other):1934942   Max.   :5.000  
 NA's         :    987   NA's   :    987                  
                                          Event_Description   Event_Time_Yr 
 Serviced Stop                                     : 930934   Min.   :2016  
 Unknown Stop                                      :   1794   1st Qu.:2016  
 UnServiced Stop                                   :1554067   Median :2016  
                                                              Mean   :2016  
                                                              3rd Qu.:2016  
                                                              Max.   :2016  
                                                                            
 Event_Time_Mth Event_Time_Date Event_Time_Day Event_Time_Hr    Event_Time_HrGroup
 Min.   :10     Min.   :3.000   Sun  :     0   Min.   : 0.00   Group6_8  :538348  
 1st Qu.:10     1st Qu.:4.000   Mon  :496190   1st Qu.: 8.00   Group15_17:497156  
 Median :10     Median :5.000   Tues :497932   Median :13.00   Group18_20:408957  
 Mean   :10     Mean   :4.999   Wed  :501611   Mean   :12.99   Group9_11 :351804  
 3rd Qu.:10     3rd Qu.:6.000   Thurs:495069   3rd Qu.:18.00   Group12_14:314050  
 Max.   :10     Max.   :7.000   Fri  :495993   Max.   :23.00   Group21_23:217259  
                                Sat  :     0                   (Other)   :159221  
 Event_Time_Min    Event_Time                  Departure_Time               
 Min.   : 0.00   Min.   :2016-10-03 00:00:09   Min.   :2016-10-03 00:00:09  
 1st Qu.:14.00   1st Qu.:2016-10-04 08:35:52   1st Qu.:2016-10-04 08:35:59  
 Median :29.00   Median :2016-10-05 13:46:00   Median :2016-10-05 13:46:06  
 Mean   :29.43   Mean   :2016-10-05 13:27:43   Mean   :2016-10-05 13:27:49  
 3rd Qu.:44.00   3rd Qu.:2016-10-06 17:57:32   3rd Qu.:2016-10-06 17:57:39  
 Max.   :59.00   Max.   :2016-10-07 23:59:59   Max.   :2016-10-08 00:12:31  
                                                                            
   Dwell_Time       Dwell_Time2         Delta_Time         Latitude    
 Min.   :   0.00   Min.   :   0.000   Min.   :-5606.0   Min.   : 0.00  
 1st Qu.:   0.00   1st Qu.:   0.000   1st Qu.:   16.0   1st Qu.:38.86  
 Median :   0.00   Median :   0.000   Median :  160.0   Median :38.90  
 Mean   :  11.86   Mean   :   5.994   Mean   :  274.1   Mean   :38.91  
 3rd Qu.:   4.00   3rd Qu.:   4.000   3rd Qu.:  402.0   3rd Qu.:38.96  
 Max.   :6205.00   Max.   :6205.000   Max.   : 9426.0   Max.   :39.19  
                                                                       
   Longitude         Heading      Odometer_Distance  Odometer_Distance_Lag1
 Min.   :-77.45   Min.   :  0.0   Min.   :       1   Min.   :       0      
 1st Qu.:-77.07   1st Qu.: 89.0   1st Qu.:  200268   1st Qu.:  198635      
 Median :-77.01   Median :180.0   Median :  394700   Median :  393026      
 Mean   :-77.02   Mean   :176.7   Mean   :  443225   Mean   :  441601      
 3rd Qu.:-76.97   3rd Qu.:269.0   3rd Qu.:  633936   3rd Qu.:  632313      
 Max.   :  0.00   Max.   :360.0   Max.   :11108034   Max.   :10853226      
                                                                           
 Odometer_Distance_Mi TravelDistance_Ft TravelDistance_Mi   TravelDistance_Mi_Hvrs
 Min.   :   0.0002    Min.   :      1   Min.   :  0.00019   Min.   : 0.0000       
 1st Qu.:  37.9295    1st Qu.:    699   1st Qu.:  0.13239   1st Qu.: 0.1034       
 Median :  74.7538    Median :   1044   Median :  0.19773   Median : 0.1378       
 Mean   :  83.9442    Mean   :   1624   Mean   :  0.30760   Mean   : 0.1918       
 3rd Qu.: 120.0635    3rd Qu.:   1518   3rd Qu.:  0.28750   3rd Qu.: 0.1828       
 Max.   :2103.7943    Max.   :1323464   Max.   :250.65606   Max.   :24.1507       
                                                                                  
    TD_Mi_q2         TD_Mi_q98       TD_Mi_SS_q5         TD_Mi_SS_q95      
 Min.   :0.05208   Min.   :0.9585   Min.   :  0.00019   Min.   :  0.00019  
 1st Qu.:0.05208   1st Qu.:0.9585   1st Qu.:  0.08848   1st Qu.:  0.25878  
 Median :0.05208   Median :0.9585   Median :  0.10608   Median :  0.32239  
 Mean   :0.05208   Mean   :0.9585   Mean   :  0.16872   Mean   :  0.47949  
 3rd Qu.:0.05208   3rd Qu.:0.9585   3rd Qu.:  0.13977   3rd Qu.:  0.42822  
 Max.   :0.05208   Max.   :0.9585   Max.   :219.16288   Max.   :246.94938  
                                                                           
 TD_Mi_SSHG_q5       TD_Mi_SSHG_q95        TD_Mi_Mean      TD_Mi_Mean_F   
 Min.   :  0.00019   Min.   :  0.00019   Min.   :0.3076   Min.   :0.2318  
 1st Qu.:  0.09167   1st Qu.:  0.24754   1st Qu.:0.3076   1st Qu.:0.2318  
 Median :  0.11395   Median :  0.31174   Median :0.3076   Median :0.2318  
 Mean   :  0.18528   Mean   :  0.46625   Mean   :0.3076   Mean   :0.2318  
 3rd Qu.:  0.15093   3rd Qu.:  0.41899   3rd Qu.:0.3076   3rd Qu.:0.2318  
 Max.   :250.65606   Max.   :250.65606   Max.   :0.3076   Max.   :0.2318  
                                                                          
 TD_Mi_SS_Mean       TD_Mi_SS_Mean_F    TD_Mi_SSHG_Mean     TD_Mi_SSHG_Mean_F
 Min.   :  0.00019   Min.   :  0.0002   Min.   :  0.00019   Min.   :  0.000  
 1st Qu.:  0.17129   1st Qu.:  0.1663   1st Qu.:  0.16760   1st Qu.:  0.163  
 Median :  0.21082   Median :  0.2058   Median :  0.20965   Median :  0.206  
 Mean   :  0.30760   Mean   :  0.2916   Mean   :  0.30760   Mean   :  0.294  
 3rd Qu.:  0.26422   3rd Qu.:  0.2582   3rd Qu.:  0.26616   3rd Qu.:  0.262  
 Max.   :219.16288   Max.   :219.1629   Max.   :250.65606   Max.   :250.656  
                     NA's   :2678                           NA's   :4904     
   TD_Mi_Med       TD_Mi_Med_F      TD_Mi_SS_Med       TD_Mi_SS_Med_F    
 Min.   :0.1977   Min.   :0.1977   Min.   :  0.00019   Min.   :  0.0002  
 1st Qu.:0.1977   1st Qu.:0.1977   1st Qu.:  0.14602   1st Qu.:  0.1458  
 Median :0.1977   Median :0.1977   Median :  0.19470   Median :  0.1947  
 Mean   :0.1977   Mean   :0.1977   Mean   :  0.28931   Mean   :  0.2827  
 3rd Qu.:0.1977   3rd Qu.:0.1977   3rd Qu.:  0.26326   3rd Qu.:  0.2633  
 Max.   :0.1977   Max.   :0.1977   Max.   :219.16288   Max.   :219.1629  
                                                       NA's   :2678      
 TD_Mi_SSHG_Med      TD_Mi_SSHG_Med_F    TD_Mi_Cnt        TD_Mi_Cnt_F     
 Min.   :  0.00019   Min.   :  0.000   Min.   :2486795   Min.   :2387406  
 1st Qu.:  0.14403   1st Qu.:  0.144   1st Qu.:2486795   1st Qu.:2387406  
 Median :  0.19527   Median :  0.195   Median :2486795   Median :2387406  
 Mean   :  0.29152   Mean   :  0.285   Mean   :2486795   Mean   :2387406  
 3rd Qu.:  0.26657   3rd Qu.:  0.266   3rd Qu.:2486795   3rd Qu.:2387406  
 Max.   :250.65606   Max.   :250.656   Max.   :2486795   Max.   :2387406  
                     NA's   :4904                                         
  TD_Mi_SS_Cnt    TD_Mi_SS_Cnt_F   TD_Mi_SSHG_Cnt   TD_Mi_SSHG_Cnt_F
 Min.   :   1.0   Min.   :   0.0   Min.   :  1.00   Min.   :  0.00  
 1st Qu.: 178.0   1st Qu.: 160.0   1st Qu.: 28.00   1st Qu.: 24.00  
 Median : 295.0   Median : 266.0   Median : 48.00   Median : 42.00  
 Mean   : 363.3   Mean   : 327.1   Mean   : 60.01   Mean   : 53.31  
 3rd Qu.: 476.0   3rd Qu.: 428.0   3rd Qu.: 78.00   3rd Qu.: 70.00  
 Max.   :1543.0   Max.   :1388.0   Max.   :663.00   Max.   :595.00  
                                                                    
 TravelTime_Sec    TT_Sec_q2    TT_Sec_q98   TT_Sec_SS_q5      TT_Sec_SS_q95     
 Min.   :    1   Min.   :10   Min.   :349   Min.   :    1.00   Min.   :    1.00  
 1st Qu.:   24   1st Qu.:10   1st Qu.:349   1st Qu.:   15.00   1st Qu.:   47.00  
 Median :   38   Median :10   Median :349   Median :   21.00   Median :   77.75  
 Mean   :  100   Mean   :10   Mean   :349   Mean   :   57.38   Mean   :  176.22  
 3rd Qu.:   70   3rd Qu.:10   3rd Qu.:349   3rd Qu.:   32.00   3rd Qu.:  129.65  
 Max.   :54551   Max.   :10   Max.   :349   Max.   :54551.00   Max.   :54551.00  
 NA's   :28                                                                      
 TT_Sec_SSHG_q5     TT_Sec_SSHG_q95     TT_Sec_Mean    TT_Sec_Mean_F  
 Min.   :    1.00   Min.   :    1.00   Min.   :104.9   Min.   :56.61  
 1st Qu.:   15.20   1st Qu.:   42.70   1st Qu.:104.9   1st Qu.:56.61  
 Median :   22.50   Median :   70.55   Median :104.9   Median :56.61  
 Mean   :   62.94   Mean   :  161.25   Mean   :104.9   Mean   :56.61  
 3rd Qu.:   34.80   3rd Qu.:  119.60   3rd Qu.:104.9   3rd Qu.:56.61  
 Max.   :54551.00   Max.   :54551.00   Max.   :104.9   Max.   :56.61  
                                                                      
 TT_Sec_SS_Mean     TT_Sec_SS_Mean_F   TT_Sec_SSHG_Mean   TT_Sec_SSHG_Mean_F
 Min.   :    1.00   Min.   :    1.00   Min.   :    1.00   Min.   :    1.00  
 1st Qu.:   28.20   1st Qu.:   26.62   1st Qu.:   27.51   1st Qu.:   26.34  
 Median :   42.61   Median :   40.46   Median :   41.76   Median :   39.96  
 Mean   :   99.62   Mean   :   86.96   Mean   :   99.55   Mean   :   88.81  
 3rd Qu.:   69.71   3rd Qu.:   66.44   3rd Qu.:   70.02   3rd Qu.:   67.22  
 Max.   :54551.00   Max.   :54551.00   Max.   :54551.00   Max.   :54551.00  
                    NA's   :2603                          NA's   :3772      
   TT_Sec_Med  TT_Sec_Med_F TT_Sec_SS_Med      TT_Sec_SS_Med_F   
 Min.   :39   Min.   :39    Min.   :    1.00   Min.   :    1.00  
 1st Qu.:39   1st Qu.:39    1st Qu.:   25.00   1st Qu.:   25.00  
 Median :39   Median :39    Median :   37.00   Median :   37.00  
 Mean   :39   Mean   :39    Mean   :   86.88   Mean   :   80.62  
 3rd Qu.:39   3rd Qu.:39    3rd Qu.:   62.00   3rd Qu.:   62.00  
 Max.   :39   Max.   :39    Max.   :54551.00   Max.   :54551.00  
                                               NA's   :2603      
 TT_Sec_SSHG_Med    TT_Sec_SSHG_Med_F    TT_Sec_Cnt       TT_Sec_Cnt_F    
 Min.   :    1.00   Min.   :    1.00   Min.   :2802888   Min.   :2705189  
 1st Qu.:   25.00   1st Qu.:   25.00   1st Qu.:2802888   1st Qu.:2705189  
 Median :   37.00   Median :   37.00   Median :2802888   Median :2705189  
 Mean   :   90.07   Mean   :   83.87   Mean   :2802888   Mean   :2705189  
 3rd Qu.:   64.00   3rd Qu.:   64.00   3rd Qu.:2802888   3rd Qu.:2705189  
 Max.   :54551.00   Max.   :54551.00   Max.   :2802888   Max.   :2705189  
                    NA's   :3772                                          
 TT_Sec_SS_Cnt    TT_Sec_SS_Cnt_F  TT_Sec_SSHG_Cnt TT_Sec_SSHG_Cnt_F
 Min.   :   1.0   Min.   :   0.0   Min.   :  1.0   Min.   :  0.00   
 1st Qu.: 200.0   1st Qu.: 183.0   1st Qu.: 30.0   1st Qu.: 27.00   
 Median : 321.0   Median : 292.0   Median : 52.0   Median : 47.00   
 Mean   : 392.4   Mean   : 357.2   Mean   : 64.7   Mean   : 58.23   
 3rd Qu.: 509.0   3rd Qu.: 464.0   3rd Qu.: 84.0   3rd Qu.: 76.00   
 Max.   :1664.0   Max.   :1523.0   Max.   :691.0   Max.   :634.00   
                                                                    
 TravelTime_Hr          TT_Hr_q2          TT_Hr_q98        TT_Hr_SS_q5       
 Min.   : 0.000278   Min.   :0.002778   Min.   :0.09694   Min.   : 0.000278  
 1st Qu.: 0.006667   1st Qu.:0.002778   1st Qu.:0.09694   1st Qu.: 0.004167  
 Median : 0.010556   Median :0.002778   Median :0.09694   Median : 0.005833  
 Mean   : 0.027782   Mean   :0.002778   Mean   :0.09694   Mean   : 0.015938  
 3rd Qu.: 0.019444   3rd Qu.:0.002778   3rd Qu.:0.09694   3rd Qu.: 0.008889  
 Max.   :15.153056   Max.   :0.002778   Max.   :0.09694   Max.   :15.153056  
 NA's   :28                                                                  
  TT_Hr_SS_q95       TT_Hr_SSHG_q5       TT_Hr_SSHG_q95        TT_Hr_Mean     
 Min.   : 0.000278   Min.   : 0.000278   Min.   : 0.000278   Min.   :0.02913  
 1st Qu.: 0.013056   1st Qu.: 0.004222   1st Qu.: 0.011861   1st Qu.:0.02913  
 Median : 0.021597   Median : 0.006250   Median : 0.019597   Median :0.02913  
 Mean   : 0.048950   Mean   : 0.017485   Mean   : 0.044792   Mean   :0.02913  
 3rd Qu.: 0.036014   3rd Qu.: 0.009667   3rd Qu.: 0.033222   3rd Qu.:0.02913  
 Max.   :15.153056   Max.   :15.153056   Max.   :15.153056   Max.   :0.02913  
                                                                              
  TT_Hr_Mean_F     TT_Hr_SS_Mean       TT_Hr_SS_Mean_F   TT_Hr_SSHG_Mean    
 Min.   :0.01573   Min.   : 0.000278   Min.   : 0.0003   Min.   : 0.000278  
 1st Qu.:0.01573   1st Qu.: 0.007832   1st Qu.: 0.0074   1st Qu.: 0.007643  
 Median :0.01573   Median : 0.011836   Median : 0.0112   Median : 0.011600  
 Mean   :0.01573   Mean   : 0.027673   Mean   : 0.0242   Mean   : 0.027654  
 3rd Qu.:0.01573   3rd Qu.: 0.019363   3rd Qu.: 0.0185   3rd Qu.: 0.019450  
 Max.   :0.01573   Max.   :15.153056   Max.   :15.1531   Max.   :15.153056  
                                       NA's   :2612                         
 TT_Hr_SSHG_Mean_F   TT_Hr_Med        TT_Hr_Med_F       TT_Hr_SS_Med      
 Min.   : 0.000    Min.   :0.01083   Min.   :0.01083   Min.   : 0.000278  
 1st Qu.: 0.007    1st Qu.:0.01083   1st Qu.:0.01083   1st Qu.: 0.006944  
 Median : 0.011    Median :0.01083   Median :0.01083   Median : 0.010278  
 Mean   : 0.025    Mean   :0.01083   Mean   :0.01083   Mean   : 0.024132  
 3rd Qu.: 0.019    3rd Qu.:0.01083   3rd Qu.:0.01083   3rd Qu.: 0.017222  
 Max.   :15.153    Max.   :0.01083   Max.   :0.01083   Max.   :15.153056  
 NA's   :3842                                                             
 TT_Hr_SS_Med_F    TT_Hr_SSHG_Med      TT_Hr_SSHG_Med_F   TT_Hr_Cnt      
 Min.   : 0.0003   Min.   : 0.000278   Min.   : 0.000   Min.   :2802888  
 1st Qu.: 0.0069   1st Qu.: 0.006944   1st Qu.: 0.007   1st Qu.:2802888  
 Median : 0.0103   Median : 0.010278   Median : 0.010   Median :2802888  
 Mean   : 0.0224   Mean   : 0.025019   Mean   : 0.023   Mean   :2802888  
 3rd Qu.: 0.0172   3rd Qu.: 0.017778   3rd Qu.: 0.018   3rd Qu.:2802888  
 Max.   :15.1531   Max.   :15.153056   Max.   :15.153   Max.   :2802888  
 NA's   :2612                          NA's   :3842                      
  TT_Hr_Cnt_F       TT_Hr_SS_Cnt    TT_Hr_SS_Cnt_F TT_Hr_SSHG_Cnt  TT_Hr_SSHG_Cnt_F
 Min.   :2705189   Min.   :   1.0   Min.   :   0   Min.   :  1.0   Min.   :  0.00  
 1st Qu.:2705189   1st Qu.: 200.0   1st Qu.: 183   1st Qu.: 30.0   1st Qu.: 27.00  
 Median :2705189   Median : 321.0   Median : 292   Median : 52.0   Median : 47.00  
 Mean   :2705189   Mean   : 392.4   Mean   : 357   Mean   : 64.7   Mean   : 58.19  
 3rd Qu.:2705189   3rd Qu.: 509.0   3rd Qu.: 464   3rd Qu.: 84.0   3rd Qu.: 76.00  
 Max.   :2705189   Max.   :1664.0   Max.   :1523   Max.   :691.0   Max.   :634.00  
                                                                                   
  SpeedAvg_Mph     
 Min.   :    0.00  
 1st Qu.:   10.10  
 Median :   16.68  
 Mean   :   26.54  
 3rd Qu.:   31.17  
 Max.   :22924.09  
 NA's   :28        

Investigation of TravelDistance_Mi.

Let’s plot just the TravelDistance_Mi values that are NOT “NA”.

TravDistMi_HistDen <- ggplot(select(TravelDistance_Mi_NoNA,
                                    TravelDistance_Mi
                                   ),
                             aes(x = TravelDistance_Mi,
                                 y = ..density..
                                )
                            ) +
  geom_histogram(binwidth = 0.05, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  coord_cartesian(xlim = c(0, 1.5), ylim = c(0, 4.0)
                 ) +
  labs(title = "Variation in Distance Between Stops",
       x = "Travel Distance (miles)",
       y = "Density"
      )
TravDistMi_HistDen

Investigation of TravelDistance_Mi.

Looking at the extremely large TravelDistance_Mi values. Some (aprox 27%) of TravelDistance_Mi values > 1 mile are when the DirChange2 changes…but what about the other ~73%?

Investigation of TravelDistance_Mi.

Any relation with DirChange2? Doesn’t look as if this is so.

ExtremeTravDist <- filter(AllDays_NewOrder,
                          !is.na(TravelDistance_Mi)
                         ) %>% 
  mutate(TravDist_Extreme = ifelse(TravelDistance_Mi > 1.1587121212, # 1.1587121212 is the 99th percentile
                                   "True",
                                   "False"
                                  )
                          ) %>% 
  group_by(DirChange2, TravDist_Extreme) %>% 
  summarise(TravDistMI_ExtCnts = n()
           )
# ExtremeTravDist
ExtremeTravDist_Spread <- as.data.frame(spread(ExtremeTravDist,
                                               TravDist_Extreme,
                                               TravDistMI_ExtCnts
                                              )
                                       ) %>% 
  select(False,
         True
        )
row.names(ExtremeTravDist_Spread) <- c("Change", "Same")
# str(ExtremeTravDist_Spread)
# ExtremeTravDist_Spread
prop.table(as.table(as.matrix(ExtremeTravDist_Spread)
                   ),
           1
          )
            False       True
Change 0.80622587 0.19377413
Same   0.98855456 0.01144544
prop.table(as.table(as.matrix(ExtremeTravDist_Spread)
                   ),
           2
          )
            False       True
Change 0.01594448 0.25169594
Same   0.98405552 0.74830406

Investigation of TravelDistance_Mi.

Looking at specific buses and StartStop_ID.

Investigation of TravelDistance_Mi & TravelDistance_Mi_New.

If TravelDisntace_Mi is below the 5th percentile for that StartStop_ID, or if TravelDisntace_Mi is above the 95th percentile for that StartStop_ID, or if TravelDistance_Mi is NA (when the BusDay_EventNum !=1), consider this an outlier. In this case, replace the value with the mean for that StartStop_ID and HourGroup (TD_Mi_SSHG_Mean_F), or if there are not enough values at the HourGroup level, replace it with the mean for that StartStop_ID.

# View(tail(AllDays_NewOrder, 500))
AllDays_NewTravelDist <- 
  mutate(AllDays_NewOrder,
         TravelDistance_Mi_New = ifelse(!is.na(TravelDistance_Mi) & 
                                          (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                                             TravelDistance_Mi > TD_Mi_SSHG_q95
                                          ) &
                                          TD_Mi_SSHG_Cnt_F >= 20,
                                        TD_Mi_SSHG_Mean_F,
                                 ifelse(!is.na(TravelDistance_Mi) & 
                                          (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                                             TravelDistance_Mi > TD_Mi_SSHG_q95
                                          ) &
                                          TD_Mi_SSHG_Cnt_F < 20 &
                                          TD_Mi_SS_Cnt_F >= 20,
                                        TD_Mi_SS_Mean_F,
                                 ifelse(!is.na(TravelDistance_Mi) & 
                                          (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                                             TravelDistance_Mi > TD_Mi_SSHG_q95
                                          ) &
                                          TD_Mi_SS_Cnt_F < 20 &
                                          TD_Mi_SS_Cnt >= 20,
                                        TD_Mi_SS_Mean,
                                 ifelse(is.na(TravelDistance_Mi) &
                                          BusDay_EventNum != 1 &
                                          TravelDistance_Mi_Hvrs != 0,
                                        TravelDistance_Mi_Hvrs,
                                 ifelse(is.na(TravelDistance_Mi) &
                                          BusDay_EventNum != 1 &
                                          TravelDistance_Mi_Hvrs == 0,
                                        TD_Mi_SS_Mean,
                                        TravelDistance_Mi
                                       ))))),
         TravelDistance_Mi_New_Label = 
           factor(ifelse(!is.na(TravelDistance_Mi) &
                           (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                              TravelDistance_Mi > TD_Mi_SSHG_q95
                           ) &
                           TD_Mi_SSHG_Cnt_F >= 20,
                         "TD_Mi_SSHG_Mean_F",
                  ifelse(!is.na(TravelDistance_Mi) &
                           (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                              TravelDistance_Mi > TD_Mi_SSHG_q95
                           ) &
                           TD_Mi_SSHG_Cnt_F < 20 &
                           TD_Mi_SS_Cnt_F >= 20,
                         "TD_Mi_SS_Mean_F",
                  ifelse(!is.na(TravelDistance_Mi) &
                           (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                              TravelDistance_Mi > TD_Mi_SSHG_q95
                           ) &
                           TD_Mi_SS_Cnt_F < 20 &
                           TD_Mi_SS_Cnt >= 20,
                         "TD_Mi_SS_Mean",
                  ifelse(is.na(TravelDistance_Mi) &
                           BusDay_EventNum != 1 &
                           TravelDistance_Mi_Hvrs != 0,
                         "TravelDistance_Mi_Hvrs",
                  ifelse(is.na(TravelDistance_Mi) &
                           BusDay_EventNum != 1 &
                           TravelDistance_Mi_Hvrs == 0,
                         "TD_Mi_SS_Mean",
                         "TravelDistance_Mi"
                        )))))
                 ),
         TravelDistance_Mi_NewHvrs = ifelse(!is.na(TravelDistance_Mi_Hvrs) &
                                              TravelDistance_Mi_Hvrs != 0 &
                                              (TravelDistance_Mi_New < TD_Mi_q2 |
                                                 TravelDistance_Mi_New > TD_Mi_q98
                                              ),
                                            TravelDistance_Mi_Hvrs,
                                            TravelDistance_Mi_New
                                           ),
         TravelDistance_Mi_NewHvrs_Label =
           factor(ifelse(!is.na(TravelDistance_Mi_Hvrs) &
                           TravelDistance_Mi_Hvrs != 0 &
                           (TravelDistance_Mi_New < TD_Mi_q2 |
                              TravelDistance_Mi_New > TD_Mi_q98
                           ),
                         "TravelDistance_Mi_Hvrs",
                         as.character(TravelDistance_Mi_New_Label)
                        )
                 ),
         SpeedAvg_Mph_NewHvrs = TravelDistance_Mi_NewHvrs / TravelTime_Hr
        )
rm(AllDays_NewOrder)
str(AllDays_NewTravelDist)
'data.frame':   2809529 obs. of  125 variables:
 $ RowNum_OG                      : int  1 3 4 5 6 7 9 10 11 12 ...
 $ UniqueLatLng                   : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ group                          : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID                   : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ BusDay_EventNum                : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Bus_ID                         : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                          : chr  "S80" "S80" "S80" "S80" ...
 $ RteChange2                     : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt                       : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ DirChange2                     : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 1 2 2 2 ...
 $ Route_Direction                : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence                  : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Start_ID                       : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc                     : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StopID_Clean                   : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator               : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_Desc                      : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ countryCode                    : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State                     : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County                    : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_City                      : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ Stop_Zip                       : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ Event_Type                     : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description              : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time_Yr                  : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth                 : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date                : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day                 : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr                  : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_HrGroup             : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Min                 : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time                     : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time                 : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time                     : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Dwell_Time2                    : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time                     : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Latitude                       : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude                      : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading                        : int  199 97 276 15 119 100 274 104 241 274 ...
 $ Odometer_Distance              : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Odometer_Distance_Lag1         : int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Odometer_Distance_Mi           : num  8.25 8.55 8.79 9.49 9.67 ...
 $ TravelDistance_Ft              : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi              : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs         : num  NA 0.15 0.105 0.165 0.832 ...
 $ TD_Mi_q2                       : num  0.0521 0.0521 0.0521 0.0521 0.0521 ...
 $ TD_Mi_q98                      : num  0.959 0.959 0.959 0.959 0.959 ...
 $ TD_Mi_SS_q5                    : num  NA 0.0252 0.2422 0.7324 0.0794 ...
 $ TD_Mi_SS_q95                   : num  NA 0.626 0.242 1.008 0.176 ...
 $ TD_Mi_SSHG_q5                  : num  NA 0.0996 0.2422 0.7002 0.1816 ...
 $ TD_Mi_SSHG_q95                 : num  NA 0.627 0.242 0.7 0.182 ...
 $ TD_Mi_Mean                     : num  0.308 0.308 0.308 0.308 0.308 ...
 $ TD_Mi_Mean_F                   : num  0.232 0.232 0.232 0.232 0.232 ...
 $ TD_Mi_SS_Mean                  : num  NaN 0.437 0.242 0.908 0.128 ...
 $ TD_Mi_SS_Mean_F                : num  NaN 0.457 0.242 0.977 NaN ...
 $ TD_Mi_SSHG_Mean                : num  NaN 0.442 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Mean_F              : num  NaN 0.491 0.242 0.7 0.182 ...
 $ TD_Mi_Med                      : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Med_F                    : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_SS_Med                   : num  NA 0.512 0.242 0.962 0.128 ...
 $ TD_Mi_SS_Med_F                 : num  NA 0.512 0.242 1.008 NA ...
 $ TD_Mi_SSHG_Med                 : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Med_F               : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_Cnt                      : int  2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 ...
 $ TD_Mi_Cnt_F                    : int  2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 ...
 $ TD_Mi_SS_Cnt                   : int  0 14 1 4 2 87 22 118 91 11 ...
 $ TD_Mi_SS_Cnt_F                 : int  0 12 1 3 0 77 18 106 81 9 ...
 $ TD_Mi_SSHG_Cnt                 : int  0 7 1 1 1 23 6 29 28 3 ...
 $ TD_Mi_SSHG_Cnt_F               : int  0 5 1 1 1 19 4 25 24 1 ...
 $ TravelTime_Sec                 : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TT_Sec_q2                      : num  10 10 10 10 10 10 10 10 10 10 ...
 $ TT_Sec_q98                     : num  349 349 349 349 349 349 349 349 349 349 ...
 $ TT_Sec_SS_q5                   : num  NA 11.9 37 30.5 172.9 ...
 $ TT_Sec_SS_q95                  : num  NA 346.3 37 75.8 189.1 ...
 $ TT_Sec_SSHG_q5                 : num  NA 59.6 37 25 190 11.6 236 51.5 55 8.8 ...
 $ TT_Sec_SSHG_q95                : num  NA 276 37 25 190 ...
 $ TT_Sec_Mean                    : num  105 105 105 105 105 ...
 $ TT_Sec_Mean_F                  : num  56.6 56.6 56.6 56.6 56.6 ...
 $ TT_Sec_SS_Mean                 : num  NaN 215.8 37 58.2 181 ...
 $ TT_Sec_SS_Mean_F               : num  NaN 218.9 37 65.5 NaN ...
 $ TT_Sec_SSHG_Mean               : num  NaN 202 37 25 190 ...
 $ TT_Sec_SSHG_Mean_F             : num  NaN 226 37 25 190 ...
 $ TT_Sec_Med                     : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Med_F                   : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_SS_Med                  : num  NA 223.5 37 65.5 181 ...
 $ TT_Sec_SS_Med_F                : num  NA 223.5 37 65.5 NA ...
 $ TT_Sec_SSHG_Med                : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_SSHG_Med_F              : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_Cnt                     : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Sec_Cnt_F                   : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TT_Sec_SS_Cnt                  : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Sec_SS_Cnt_F                : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TT_Sec_SSHG_Cnt                : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Sec_SSHG_Cnt_F              : int  0 5 1 1 1 31 4 32 32 1 ...
 $ TravelTime_Hr                  : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ TT_Hr_q2                       : num  0.00278 0.00278 0.00278 0.00278 0.00278 ...
 $ TT_Hr_q98                      : num  0.0969 0.0969 0.0969 0.0969 0.0969 ...
 $ TT_Hr_SS_q5                    : num  NA 0.00331 0.01028 0.00849 0.04803 ...
 $ TT_Hr_SS_q95                   : num  NA 0.0962 0.0103 0.0211 0.0525 ...
  [list output truncated]

Investigation of TravelDistance_Mi & TravelDistance_Mi_Hvrs & TravelDistance_Mi_New.

Quick summary and then correlation calculation.

message("BusDay_EventNum != 1")
BusDay_EventNum != 1
summary(select(filter(AllDays_NewTravelDist,
                      BusDay_EventNum != 1
                     ),
               TravelDistance_Mi,
               TravelDistance_Mi_Hvrs,
               TravelDistance_Mi_New,
               TravelDistance_Mi_NewHvrs
              )
       )
 TravelDistance_Mi TravelDistance_Mi_Hvrs TravelDistance_Mi_New
 Min.   :  0.00    Min.   : 0.0000        Min.   :  0.00019    
 1st Qu.:  0.13    1st Qu.: 0.1055        1st Qu.:  0.14072    
 Median :  0.20    Median : 0.1424        Median :  0.19867    
 Mean   :  0.31    Mean   : 0.2008        Mean   :  0.29751    
 3rd Qu.:  0.29    3rd Qu.: 0.1935        3rd Qu.:  0.27633    
 Max.   :250.66    Max.   :24.4068        Max.   :250.65606    
 NA's   :316206                           NA's   :38           
 TravelDistance_Mi_NewHvrs
 Min.   : 0.00019         
 1st Qu.: 0.14205         
 Median : 0.19903         
 Mean   : 0.25859         
 3rd Qu.: 0.27557         
 Max.   :36.23636         
 NA's   :38               
message("All records")
All records
cor(select(AllDays_NewTravelDist,
           TravelDistance_Mi,
           TravelDistance_Mi_Hvrs,
           TravelDistance_Mi_New,
           TravelDistance_Mi_NewHvrs
          ),
    use = "pairwise.complete.obs"
  )
                          TravelDistance_Mi TravelDistance_Mi_Hvrs
TravelDistance_Mi                 1.0000000              0.5447660
TravelDistance_Mi_Hvrs            0.5447660              1.0000000
TravelDistance_Mi_New             0.9513379              0.5837182
TravelDistance_Mi_NewHvrs         0.6005944              0.9005277
                          TravelDistance_Mi_New TravelDistance_Mi_NewHvrs
TravelDistance_Mi                     0.9513379                 0.6005944
TravelDistance_Mi_Hvrs                0.5837182                 0.9005277
TravelDistance_Mi_New                 1.0000000                 0.6346981
TravelDistance_Mi_NewHvrs             0.6346981                 1.0000000

Investigation of TravelDistance_Mi_NewHvrs_Label & TravelDistance_Mi_NewHvrs_Label.

Show how the labels changed.

group_by(AllDays_NewTravelDist,
         TravelDistance_Mi_New_Label,
         TravelDistance_Mi_NewHvrs_Label
        ) %>% 
  summarise(CntNum = n(),
            CntPct = format(CntNum / nrow(AllDays_NewTravelDist),
                            scientific = 9999
                           )
           ) %>% 
  arrange(desc(CntPct)
         )

Investigation of TravelDistance_Mi & TravelDistance_Mi_Hvrs & TravelDistance_Mi_New.

Graphing the two methods of calculating TravelDistance_Mi.

First, let’s get create a function to plot the liner model equation.

lm_eqn <- function(df, y, x){
  m <- lm(y ~ x, df)
  
  l <- list(a = format(coef(m)[1], digits = 2),
            b = format(abs(coef(m)[2]), digits = 2),
            s1 = ifelse(test = coef(m)[2] > 0,
                        yes = "+",
                        no = "-"
                       ),
            r2 = format(summary(m)$r.squared,
                        digits = 3
                       )
           )
  
  eq <- substitute(italic(y) == a~~s1~~b %.% italic(x)*","~~italic(r)^2~"="~r2,
                   l
                  )
  
  as.character(as.expression(eq)
              )             
}

Investigation of TravelDistance_Mi & TravelDistance_Mi_NewHvrs.

Scatter plot (using a 10% sample to making plotting time faster and to reduce un-needed data in the “same” splot).

set.seed(123456789)
AllDays_NewTravelDist_10Pct <- filter(AllDays_NewTravelDist,
                                      !is.na(TravelDistance_Mi_NewHvrs) &
                                        !is.na(TravelDistance_Mi)
                                     ) %>% 
  rename(DistMethod = TravelDistance_Mi_NewHvrs_Label) %>% 
  sample_frac(0.1)
TravDist_MiVsCalc <- ggplot(select(AllDays_NewTravelDist_10Pct,
                                   TravelDistance_Mi_NewHvrs,
                                   TravelDistance_Mi,
                                   DistMethod
                                  ),
                            aes(x = TravelDistance_Mi,
                                y = TravelDistance_Mi_NewHvrs,
                                colour = DistMethod
                               )
                           ) +
  scale_colour_manual(values = c("red","blue", "green", "orange", "black")
                     ) +
  geom_point(shape = 1, alpha = 0.5) +
  scale_shape(solid = FALSE) +
  geom_smooth(method = "lm", colour = "blue") +
  geom_abline(intercept = 0, slope = 1, colour = "red") +
  coord_cartesian(xlim = c(0, 1.5), ylim = c(0, 1.5)
                 ) +
  scale_x_continuous(breaks = seq(0, 1.5, 0.25)
                    ) +
  scale_y_continuous(breaks = seq(0, 1.5, 0.25)
                    ) +
  theme(legend.position = "bottom", #c(0.85, 0.40),
        legend.text = element_text(size = 6)
       ) +
  annotate(label = lm_eqn(df = AllDays_NewTravelDist_10Pct,
                          x = AllDays_NewTravelDist_10Pct$TravelDistance_Mi,
                          y = AllDays_NewTravelDist_10Pct$TravelDistance_Mi_NewHvrs
                         ),
           # x = 62,
           # y = 20,
           x = 0.70,
           y = 0.00,
           geom = "text",
           size = 3,
           colour = "blue",
           parse = TRUE
          ) +
  annotate(label = "Reference Line (slope = 1)",
           # x = 16,
           # y = 30,
           x = 0.80,
           y = 1.05,
           geom = "text",
           size = 3,
           colour = "red"
          ) +
  labs(title = "TravelDistance_Mi vs. TravelDistance_Mi_NewHvrs",
       x = "TravelDistance_Mi",
       y = "TravelDistance_Mi_NewHvrs"
      )
# +
#   geom_jitter()
TravDist_MiVsCalc

Investigation of TravelDistance_Mi & TravelDistance_Mi_Hvrs & TravelDistance_Mi_New.

Graphing test with rbokeh.

Investigation of TravelDistance_Mi_New.

Calculating the minimum TravelDistance_Mi_New value at each percentile.

rm(TravDist_MiVsCalc_Bokeh)
rm(AllDays_NewTravelDist_10Pct)
summary(select(AllDays_NewTravelDist,
               TravelDistance_Mi,
               TravelDistance_Mi_Hvrs,
               TravelDistance_Mi_New,
               TravelDistance_Mi_NewHvrs
              )
       )
 TravelDistance_Mi TravelDistance_Mi_Hvrs TravelDistance_Mi_New
 Min.   :  0.0     Min.   : 0.000         Min.   :  0.000      
 1st Qu.:  0.1     1st Qu.: 0.106         1st Qu.:  0.141      
 Median :  0.2     Median : 0.142         Median :  0.199      
 Mean   :  0.3     Mean   : 0.201         Mean   :  0.298      
 3rd Qu.:  0.3     3rd Qu.: 0.193         3rd Qu.:  0.276      
 Max.   :250.7     Max.   :24.407         Max.   :250.656      
 NA's   :322734    NA's   :6528           NA's   :6566         
 TravelDistance_Mi_NewHvrs
 Min.   : 0.000           
 1st Qu.: 0.142           
 Median : 0.199           
 Mean   : 0.259           
 3rd Qu.: 0.276           
 Max.   :36.236           
 NA's   :6566             
summary(select(filter(AllDays_NewTravelDist,
                      BusDay_EventNum != 1
                     ),
               TravelDistance_Mi,
               TravelDistance_Mi_Hvrs,
               TravelDistance_Mi_New,
               TravelDistance_Mi_NewHvrs
              )
       )
 TravelDistance_Mi TravelDistance_Mi_Hvrs TravelDistance_Mi_New
 Min.   :  0.00    Min.   : 0.0000        Min.   :  0.00019    
 1st Qu.:  0.13    1st Qu.: 0.1055        1st Qu.:  0.14072    
 Median :  0.20    Median : 0.1424        Median :  0.19867    
 Mean   :  0.31    Mean   : 0.2008        Mean   :  0.29751    
 3rd Qu.:  0.29    3rd Qu.: 0.1935        3rd Qu.:  0.27633    
 Max.   :250.66    Max.   :24.4068        Max.   :250.65606    
 NA's   :316206                           NA's   :38           
 TravelDistance_Mi_NewHvrs
 Min.   : 0.00019         
 1st Qu.: 0.14205         
 Median : 0.19903         
 Mean   : 0.25859         
 3rd Qu.: 0.27557         
 Max.   :36.23636         
 NA's   :38               
TravDistMiN_Ntile <- as.data.frame(select(AllDays_NewTravelDist,
                                          StartStop_ID,
                                          TravelDistance_Mi_New_Label,
                                          # TravelDistance_Mi_NewHvrs_Label,
                                          TravelDistance_Mi_New
                                          # TravelDistance_Mi_NewHvrs
                                         )
                                  ) %>% 
  mutate(PctR_N = percent_rank(AllDays_NewTravelDist$TravelDistance_Mi_New),
         # PctR_H = percent_rank(AllDays_NewTravelDist$TravelDistance_Mi_NewHvrs),
         PctR_Round_N = round(PctR_N, 2)
         # PctR_Round_H = round(PctR_H, 2)
        ) 
# str(TravDistMiN_Ntile)
# View(head(TravDistMiN_Ntile, 500))
TravDistMiN_Ntile_Rows <- nrow(TravDistMiN_Ntile)
# View(tail(TravDistMiN_Ntile, 500))
TravDistMiN_Pctiles <- group_by(TravDistMiN_Ntile,
                                PctR_Round_N
                               ) %>% 
  summarise(
    MinTDMiAtPctile_N = min(TravelDistance_Mi_New),
    # MinTDMiAtPctile_H = min(TravelDistance_Mi_NewHvrs),
    CntsAtPctile_N = sum(!is.na(TravelDistance_Mi_New)),
    # CntsAtPctile_H = sum(!is.na(TravelDistance_Mi_NewHvrs)),
    PctsAtPctile_N = CntsAtPctile_N / TravDistMiN_Ntile_Rows
    # PctsAtPctile_H = CntsAtPctile_H / TravDistMiN_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP_N = cumsum(PctsAtPctile_N)
         # CumSumPAtP_H = cumsum(PctsAtPctile_H)
        )
# View(TravDistMiN_Pctiles)
TravDistMiN_Pctiles

Investigation of TravelDistance_Mi_NewHvrs

Calculating the minimum TravelDistance_Mi_NewHvrs value at each percentile.

TravDistMiH_Ntile <- as.data.frame(select(AllDays_NewTravelDist,
                                          StartStop_ID,
                                          # TravelDistance_Mi_New_Label,
                                          TravelDistance_Mi_NewHvrs_Label,
                                          # TravelDistance_Mi_New,
                                          TravelDistance_Mi_NewHvrs
                                         )
                                  ) %>% 
  mutate(# PctR_N = percent_rank(AllDays_NewTravelDist$TravelDistance_Mi_New),
         PctR_H = percent_rank(AllDays_NewTravelDist$TravelDistance_Mi_NewHvrs),
         # PctR_Round_N = round(PctR_N, 2),
         PctR_Round_H = round(PctR_H, 2)
        ) 
# str(TravDistMiH_Ntile)
# View(head(TravDistMiH_Ntile, 500))
TravDistMiH_Ntile_Rows <- nrow(TravDistMiH_Ntile)
# View(tail(TravDistMiH_Ntile, 500))
TravDistMiH_Pctiles <- group_by(TravDistMiH_Ntile,
                                PctR_Round_H
                               ) %>% 
  summarise(
    # MinTDMiAtPctile_N = min(TravelDistance_Mi_New),
    MinTDMiAtPctile_H = min(TravelDistance_Mi_NewHvrs),
    # CntsAtPctile_N = sum(!is.na(TravelDistance_Mi_New)),
    CntsAtPctile_H = sum(!is.na(TravelDistance_Mi_NewHvrs)),
    # PctsAtPctile_N = CntsAtPctile_N / TravDistMiH_Ntile_Rows,
    PctsAtPctile_H = CntsAtPctile_H / TravDistMiH_Ntile_Rows
  ) %>% 
  mutate(# CumSumPAtP_N = cumsum(PctsAtPctile_N),
         CumSumPAtP_H = cumsum(PctsAtPctile_H)
        )
# View(TravDistMiH_Pctiles)
TravDistMiH_Pctiles

Join TravDistMiH_Pctiles, TravDistMiN_Pctiles, and TravDistMi_Pctiles.

~11% of rides are still showing as less than 0.1 miles of TravelDistance_Mi_NewHvrs.

rm(TravDistMiN_Ntile_Rows, TravDistMiH_Ntile_Rows, TravDistMiN_Ntile, TravDistMiH_Ntile)
# View(TravDistMi_Pctiles)
# View(TravDistMiN_Pctiles)
# View(TravDistMiH_Pctiles)
TravDistMi_Pctiles_All <- inner_join(x = TravDistMi_Pctiles,
                                     y = TravDistMiN_Pctiles,
                                     by = c("PctR_Round" = "PctR_Round_N")
                                    ) %>% 
  inner_join(y = TravDistMiH_Pctiles,
             by = c("PctR_Round" = "PctR_Round_H")
            ) %>% 
  select(PctR_Round,
         MinTravDistMiAtPctile,
         MinTDMiAtPctile_N,
         MinTDMiAtPctile_H,
         CntsAtPctile,
         CntsAtPctile_N,
         CntsAtPctile_H,
         PctsAtPctile,
         PctsAtPctile_N,
         PctsAtPctile_H,
         CumSumPAtP,
         CumSumPAtP_N,
         CumSumPAtP_H
         )
# str(TravDistMi_Pctiles_All)
rm(TravDistMi_Pctiles, TravDistMiN_Pctiles,TravDistMiH_Pctiles)
View(TravDistMi_Pctiles_All)
TravDistMi_Pctiles_All

Investigation of TravelDistance_Mi_New.

Why are there still some small or large TravelDistance_Mi_NewHvrs values.

Investigation of TravelTime_Hr.

View(TravDistMi_Pctiles): 98% of TravelTime_Hr are between 7 seconds and 464 seconds (~8 minutes).

TravTimeHr_Ntile <- select(AllDays_NewTravelDist,
                           TravelTime_Hr
                          ) %>% 
  mutate(# Pctile = ntile(AllDays_NewTravelDist$TravelTime_Hr, 100),
         # MinR = min_rank(AllDays_NewTravelDist$TravelTime_Hr),
         PctR = percent_rank(AllDays_NewTravelDist$TravelTime_Hr),
         PctR_Round = round(PctR, 2)
        ) 
# str(TravTimeHr_Ntile)
TravTimeHr_Ntile_Rows <- nrow(TravTimeHr_Ntile)
# View(tail(TravTimeHr_Ntile, 500))
TravTimeHr_Pctiles <- group_by(TravTimeHr_Ntile,
                               PctR_Round
                              ) %>% 
  summarise(
    MinTravTimeHrAtPctile = min(TravelTime_Hr),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / TravTimeHr_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile),
         MinTravTimeSecAtPctile = MinTravTimeHrAtPctile * 3600
        )
rm(TravTimeHr_Ntile_Rows)
rm(TravTimeHr_Ntile)
View(TravTimeHr_Pctiles)
TravTimeHr_Pctiles

Investigation of TravelTime_Hr.

Histogram of TravelTime_Sec.

TravTime_Sec_HistDen <- ggplot(filter(select(AllDays_NewTravelDist,
                                             TravelTime_Sec
                                            ),
                                      !is.na(TravelTime_Sec)
                                     ),
                               aes(x = TravelTime_Sec,
                                   y = ..density..
                                  )
                          ) +
  geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  # stat_bin(binwidth = 5,
  #          geom = "text",
  #          size = 2.5,
  #          vjust = 1.5,
  #          aes(label = format(..count.., big.mark = ",")
  #             ),
  #         ) +
  coord_cartesian(xlim = c(0, 180), ylim = c(0, 0.02)
                 ) +
  #  theme(legend.position="none") +
  labs(title = "Variation in Travel Time",
       x = "Travel Time (sec)",
       y = "Density"
      )
TravTime_Sec_HistDen

Investigation of TravelTime_Sec.

TravelTime_Sec values are NA.

Investigation of TravelTime_Sec.

TravelTime_Sec values are extremely small.

Investigation of TravelTime_Sec.

TravelTime_Sec values are extremely large.

Investigation of TravelTime_Sec.

Are large TravelTime_Sec values related to RouteChanges? Looks likely. When the Bus involves a Route “change”, there is almost twice as likely to be a case of an outlier TravelTime_Sec value (on the high side).

TTLargeRteChng <- select(AllDays_NewTravelDist,
                         -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
                        ) %>% 
  mutate(TT_Out = factor(ifelse(TravelTime_Sec > 464,  # this is the 99th percentile
                                "Outlier",
                                "Normal"
                               )
                        )
        )
# str(TTLargeRteChng)
TTLargeRteChng_Cnts <- group_by(TTLargeRteChng,
                                RteChange2,
                                TT_Out
                               ) %>% 
  summarise(Cnts = n()
           )
TTLargeRteChng_Spread <- as.data.frame(spread(TTLargeRteChng_Cnts,
                                              TT_Out,
                                              Cnts
                                             )
                                      ) %>%
  select(-RteChange2)
row.names(TTLargeRteChng_Spread) <- c("Change", "Same")
# str(TTLargeRteChng_Spread)
# When the Bus involves a Route "change", there is almost twice as likely to be a case of an outlier TravelTime_Sec value.
TTLargeRteChng_Spread
prop.table(as.table(as.matrix(TTLargeRteChng_Spread)
                   ),
           1
          )
             Normal      Outlier         <NA>
Change 2.583712e-01 4.669780e-01 2.746508e-01
Same   9.889061e-01 1.105373e-02 4.020451e-05
prop.table(as.table(as.matrix(TTLargeRteChng_Spread)
                   ),
           2
          )
            Normal     Outlier        <NA>
Change 0.002224561 0.264978279 0.983135070
Same   0.997775439 0.735021721 0.016864930
# rm(TTLargeRteChng, TTLargeRteChng_Spread)
         

Investigation of TravelTime_Sec.

Are large TravelTime_Sec values related to RouteChanges? Looks likely.

Investigation of TravelTime_Sec.

If TravelTime_Sec is below the 5th percentile for that StartStop_ID, or if TravelTime_Sec is above the 95th percentile for that StartStop_ID, consider this an outlier. In this case, replace the value with the mean for that StartStop_ID and HourGroup (TT_Sec_SSHG_Mean_F), or if there are not enough values at the HourGroup level, replace it with the mean for that StartStop_ID.

rm(TTLargeRteChng, TTLargeRteChng_Cnts, TTLargeRteChng_Spread)
NewTravTime <- mutate(AllDays_NewTravelDist,
                      TT_Sec_New = ifelse(!is.na(TravelTime_Sec) &
                                            (TravelTime_Sec < TT_Sec_SSHG_q5 |
                                               TravelTime_Sec > TT_Sec_SSHG_q95
                                            ) &
                                            TT_Sec_SSHG_Cnt_F >= 20,
                                          TT_Sec_SSHG_Mean_F,
                                   ifelse(!is.na(TravelTime_Sec) &
                                            (TravelTime_Sec < TT_Sec_SSHG_q5 |
                                               TravelTime_Sec > TT_Sec_SSHG_q95
                                            ) &
                                            TT_Sec_SSHG_Cnt_F < 20 &
                                            TT_Sec_SS_Cnt_F >= 20,
                                          TT_Sec_SS_Mean_F,
                                   ifelse(!is.na(TravelTime_Sec) &
                                            (TravelTime_Sec < TT_Sec_SSHG_q5 |
                                               TravelTime_Sec > TT_Sec_SSHG_q95
                                            ) &
                                            TT_Sec_SS_Cnt_F < 20 &
                                            TT_Sec_SS_Cnt >= 20,
                                          TT_Sec_SS_Mean,
                                   ifelse(!is.na(TravelTime_Sec) &
                                            (TravelTime_Sec < TT_Sec_SSHG_q5 |
                                               TravelTime_Sec > TT_Sec_SSHG_q95
                                            ) &
                                            TT_Sec_SS_Cnt_F < 20 &
                                            TT_Sec_SS_Cnt < 20 &
                                            RteChange2 == "Change",
                                          NA,
                                          TravelTime_Sec
                                         )))),
                      
                      TT_Sec_New_Label = 
           factor(ifelse(!is.na(TravelTime_Sec) &
                           (TravelTime_Sec < TT_Sec_SSHG_q5 |
                              TravelTime_Sec > TT_Sec_SSHG_q95
                           ) &
                           TT_Sec_SSHG_Cnt_F >= 20,
                         "TT_Sec_SSHG_Mean_F",
                  ifelse(!is.na(TravelTime_Sec) &
                           (TravelTime_Sec < TT_Sec_SSHG_q5 |
                              TravelTime_Sec > TT_Sec_SSHG_q95
                           ) &
                           TT_Sec_SSHG_Cnt_F < 20 &
                           TT_Sec_SS_Cnt_F >= 20,
                         "TT_Sec_SS_Mean_F",
                  ifelse(!is.na(TravelTime_Sec) &
                           (TravelTime_Sec < TT_Sec_SSHG_q5 |
                              TravelTime_Sec > TT_Sec_SSHG_q95
                            ) &
                           TT_Sec_SS_Cnt_F < 20 &
                           TT_Sec_SS_Cnt >= 20,
                         "TT_Sec_SS_Mean",
                  ifelse(!is.na(TravelTime_Sec) &
                           (TravelTime_Sec < TT_Sec_SSHG_q5 |
                              TravelTime_Sec > TT_Sec_SSHG_q95
                           ) &
                           TT_Sec_SS_Cnt_F < 20 &
                           TT_Sec_SS_Cnt < 20 &
                           RteChange2 == "Change",
                         NA,
                         "TravelTime_Sec"
                        ))))
                 ),
                  
                  TT_Hr_New = TT_Sec_New / (60 * 60)
           )
dim(AllDays_NewTravelDist)
[1] 2809529     125
dim(NewTravTime)
[1] 2809529     128
rm(AllDays_NewTravelDist)
summary(select(NewTravTime,
           -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )
   RowNum_OG       UniqueLatLng       group      StartStop_ID      
 Min.   :      1   Length:2809529     1:559521   Length:2809529    
 1st Qu.: 784722   Class :character   2:561389   Class :character  
 Median :1563300   Mode  :character   3:567794   Mode  :character  
 Mean   :1562504                      4:559180                     
 3rd Qu.:2337981                      5:561645                     
 Max.   :3119443                                                   
                                                                   
 BusDay_EventNum      Bus_ID        Route            RteChange2     
 Min.   :   1.0   Min.   :  11   Length:2809529     Change:  23772  
 1st Qu.: 113.0   1st Qu.:2922   Class :character   Same  :2785757  
 Median : 248.0   Median :6195   Mode  :character                   
 Mean   : 290.5   Mean   :5382                                      
 3rd Qu.: 428.0   3rd Qu.:7104                                      
 Max.   :1344.0   Max.   :8105                                      
                                                                    
    RouteAlt        DirChange2      Route_Direction   Stop_Sequence   
 2      :1128810   Change:  65126   SOUTH   :739235   Min.   :  1.00  
 1      :1065425   Same  :2744403   NORTH   :735203   1st Qu.: 12.00  
 3      : 260372                    WEST    :649706   Median : 24.00  
 4      : 130801                    EAST    :628074   Mean   : 26.83  
 5      :  75039                    LOOP    : 35611   3rd Qu.: 39.00  
 6      :  56408                    CLOCKWIS: 10671   Max.   :104.00  
 (Other):  92674                    (Other) : 11029                   
   Start_ID          Start_Desc        StopID_Clean       StopID_Indicator
 Length:2809529     Length:2809529     Length:2809529     ID_Bad:  18948  
 Class :character   Class :character   Class :character   ID_OK :2790581  
 Mode  :character   Mode  :character   Mode  :character                   
                                                                          
                                                                          
                                                                          
                                                                          
  Stop_Desc         countryCode    Stop_State                   Stop_County     
 Length:2809529     US  :2808431   DC  :1297006   District of Columbia:1297006  
 Class :character   NA's:   1098   MD  : 982401   Prince George's     : 589193  
 Mode  :character                  VA  : 529024   Montgomery          : 391422  
                                   NA's:   1098   Fairfax             : 204558  
                                                  Arlington           : 198618  
                                                  (Other)             : 127634  
                                                  NA's                :   1098  
         Stop_City          Stop_Zip         Event_Type 
 Washington   :1296626   20020  : 156333   Min.   :3.0  
 Silver Spring: 227570   20032  : 117215   1st Qu.:3.0  
 Arlington    : 198360   20019  : 116560   Median :4.0  
 Hyattsville  : 166930   20011  : 114518   Mean   :3.6  
 Alexandria   : 103776   20002  : 101086   3rd Qu.:4.0  
 (Other)      : 815169   (Other):2202719   Max.   :5.0  
 NA's         :   1098   NA's   :   1098                
                                          Event_Description   Event_Time_Yr 
 Serviced Stop                                     :1127366   Min.   :2016  
 Unknown Stop                                      :   2579   1st Qu.:2016  
 UnServiced Stop                                   :1679584   Median :2016  
                                                              Mean   :2016  
                                                              3rd Qu.:2016  
                                                              Max.   :2016  
                                                                            
 Event_Time_Mth Event_Time_Date Event_Time_Day Event_Time_Hr    Event_Time_HrGroup
 Min.   :10     Min.   :3.000   Sun  :     0   Min.   : 0.00   Group6_8  :611612  
 1st Qu.:10     1st Qu.:4.000   Mon  :559521   1st Qu.: 8.00   Group15_17:560103  
 Median :10     Median :5.000   Tues :561389   Median :13.00   Group18_20:461056  
 Mean   :10     Mean   :5.001   Wed  :567794   Mean   :12.97   Group9_11 :396514  
 3rd Qu.:10     3rd Qu.:6.000   Thurs:559180   3rd Qu.:18.00   Group12_14:353603  
 Max.   :10     Max.   :7.000   Fri  :561645   Max.   :23.00   Group21_23:244522  
                                Sat  :     0                   (Other)   :182119  
 Event_Time_Min    Event_Time                  Departure_Time               
 Min.   : 0.00   Min.   :2016-10-03 00:00:00   Min.   :2016-10-03 00:00:00  
 1st Qu.:14.00   1st Qu.:2016-10-04 08:36:14   1st Qu.:2016-10-04 08:36:20  
 Median :29.00   Median :2016-10-05 13:49:29   Median :2016-10-05 13:49:38  
 Mean   :29.43   Mean   :2016-10-05 13:29:21   Mean   :2016-10-05 13:29:28  
 3rd Qu.:44.00   3rd Qu.:2016-10-06 17:58:06   3rd Qu.:2016-10-06 17:58:13  
 Max.   :59.00   Max.   :2016-10-07 23:59:59   Max.   :2016-10-08 00:12:31  
                                                                            
   Dwell_Time       Dwell_Time2         Delta_Time         Latitude    
 Min.   :   0.00   Min.   :   0.000   Min.   :-5606.0   Min.   : 0.00  
 1st Qu.:   0.00   1st Qu.:   0.000   1st Qu.:   14.0   1st Qu.:38.86  
 Median :   0.00   Median :   0.000   Median :  157.0   Median :38.90  
 Mean   :  12.56   Mean   :   6.359   Mean   :  268.8   Mean   :38.91  
 3rd Qu.:   5.00   3rd Qu.:   4.000   3rd Qu.:  396.0   3rd Qu.:38.96  
 Max.   :6205.00   Max.   :6205.000   Max.   : 9426.0   Max.   :39.19  
                                                                       
   Longitude         Heading      Odometer_Distance  Odometer_Distance_Lag1
 Min.   :-77.45   Min.   :  0.0   Min.   :       0   Min.   :       0      
 1st Qu.:-77.07   1st Qu.: 89.0   1st Qu.:  177595   1st Qu.:  177326      
 Median :-77.02   Median :180.0   Median :  377510   Median :  376934      
 Mean   :-77.02   Mean   :176.9   Mean   :  426254   Mean   :  425713      
 3rd Qu.:-76.97   3rd Qu.:269.0   3rd Qu.:  623667   3rd Qu.:  622879      
 Max.   :  0.00   Max.   :360.0   Max.   :11108034   Max.   :10853226      
                                                     NA's   :6528          
 Odometer_Distance_Mi TravelDistance_Ft TravelDistance_Mi TravelDistance_Mi_Hvrs
 Min.   :   0.00      Min.   :      1   Min.   :  0.0     Min.   : 0.000        
 1st Qu.:  33.64      1st Qu.:    699   1st Qu.:  0.1     1st Qu.: 0.106        
 Median :  71.50      Median :   1044   Median :  0.2     Median : 0.142        
 Mean   :  80.73      Mean   :   1624   Mean   :  0.3     Mean   : 0.201        
 3rd Qu.: 118.12      3rd Qu.:   1518   3rd Qu.:  0.3     3rd Qu.: 0.193        
 Max.   :2103.79      Max.   :1323464   Max.   :250.7     Max.   :24.407        
                      NA's   :322734    NA's   :322734    NA's   :6528          
 TravelTime_Sec    TravelTime_Hr     SpeedAvg_Mph     TravelDistance_Mi_New
 Min.   :    1.0   Min.   : 0.000   Min.   :    0.0   Min.   :  0.000      
 1st Qu.:   25.0   1st Qu.: 0.007   1st Qu.:   10.1   1st Qu.:  0.141      
 Median :   39.0   Median : 0.011   Median :   16.7   Median :  0.199      
 Mean   :  104.9   Mean   : 0.029   Mean   :   26.5   Mean   :  0.298      
 3rd Qu.:   72.0   3rd Qu.: 0.020   3rd Qu.:   31.2   3rd Qu.:  0.276      
 Max.   :60750.0   Max.   :16.875   Max.   :22924.1   Max.   :250.656      
 NA's   :6641      NA's   :6641     NA's   :322762    NA's   :6566         
         TravelDistance_Mi_New_Label TravelDistance_Mi_NewHvrs
 TD_Mi_SS_Mean         :   1425      Min.   : 0.000           
 TD_Mi_SS_Mean_F       :  68790      1st Qu.: 0.142           
 TD_Mi_SSHG_Mean_F     : 235643      Median : 0.199           
 TravelDistance_Mi     :2187829      Mean   : 0.259           
 TravelDistance_Mi_Hvrs: 315842      3rd Qu.: 0.276           
                                     Max.   :36.236           
                                     NA's   :6566             
       TravelDistance_Mi_NewHvrs_Label SpeedAvg_Mph_NewHvrs   TT_Sec_New      
 TD_Mi_SS_Mean         :   1258        Min.   :   0.00      Min.   :    1.00  
 TD_Mi_SS_Mean_F       :  67174        1st Qu.:  10.09      1st Qu.:   25.00  
 TD_Mi_SSHG_Mean_F     : 232871        Median :  16.60      Median :   39.00  
 TravelDistance_Mi     :2122413        Mean   :  24.11      Mean   :   91.31  
 TravelDistance_Mi_Hvrs: 385813        3rd Qu.:  28.83      3rd Qu.:   70.00  
                                       Max.   :8691.73      Max.   :60750.00  
                                       NA's   :6667         NA's   :9218      
           TT_Sec_New_Label     TT_Hr_New     
 TravelTime_Sec    :2503794   Min.   : 0.000  
 TT_Sec_SS_Mean    :    804   1st Qu.: 0.007  
 TT_Sec_SS_Mean_F  :  52426   Median : 0.011  
 TT_Sec_SSHG_Mean_F: 249928   Mean   : 0.025  
 NA's              :   2577   3rd Qu.: 0.019  
                              Max.   :16.875  
                              NA's   :9218    
str(select(NewTravTime,
           TravelTime_Sec,
           TT_Sec_New,
           TT_Sec_New_Label,
           TT_Hr_New
          )
   )
'data.frame':   2809529 obs. of  4 variables:
 $ TravelTime_Sec  : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TT_Sec_New      : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TT_Sec_New_Label: Factor w/ 4 levels "TravelTime_Sec",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ TT_Hr_New       : num  NA 0.05 0.01028 0.00694 0.05278 ...
summary(select(NewTravTime,
               TravelTime_Sec,
               TT_Sec_New,
               TT_Sec_New_Label,
               TT_Hr_New
              )
       )
 TravelTime_Sec      TT_Sec_New                 TT_Sec_New_Label     TT_Hr_New     
 Min.   :    1.0   Min.   :    1.00   TravelTime_Sec    :2503794   Min.   : 0.000  
 1st Qu.:   25.0   1st Qu.:   25.00   TT_Sec_SS_Mean    :    804   1st Qu.: 0.007  
 Median :   39.0   Median :   39.00   TT_Sec_SS_Mean_F  :  52426   Median : 0.011  
 Mean   :  104.9   Mean   :   91.31   TT_Sec_SSHG_Mean_F: 249928   Mean   : 0.025  
 3rd Qu.:   72.0   3rd Qu.:   70.00   NA's              :   2577   3rd Qu.: 0.019  
 Max.   :60750.0   Max.   :60750.00                                Max.   :16.875  
 NA's   :6641      NA's   :9218                                    NA's   :9218    

Test investigation of just the X2 Route. Box plots for time between bus arrivals (by HourGroup).

Test investigation of just the X2 Route. Violin plots for time between bus arrivals (by Hour Group).

TimeBtwEvents_X2_ViolinPlot <- ggplot(select(as.data.frame(X2_ByStop),
                                             TimeToEvent_Min,
                                             Event_Time_HrGroup
                                             ),
                                      aes(factor(Event_Time_HrGroup),
                                          TimeToEvent_Min,
                                          fill = factor(Event_Time_HrGroup)
                                         )
                                     ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = Count_Values,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 80)
                 ) +
  labs(title = "How Often an X2 Arrives at a Given Stop",
       x = "Hour Group",
       y = "Time Between Busses (min)"
      )
TimeBtwEvents_X2_ViolinPlot

Test investigation of just the X2 Route. Box plots for time between bus arrivals (by Zip Code).

# Count_Values is needed to display the medians on the box plots
Count_Values_z <- ddply(as.data.frame(X2_ByStop),
                        .(Stop_Zip),
                        summarise,
                        Value_Counts = median(TimeToEvent_Min, na.rm = TRUE)
                       )
TimeBtwEvents_X2_BoxPlot_z <- ggplot(select(as.data.frame(X2_ByStop),
                                            TimeToEvent_Min,
                                            Stop_Zip
                                           ),
                                     aes(factor(Stop_Zip),
                                         TimeToEvent_Min,
                                         fill = factor(Stop_Zip)
                                        )
                                    ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE, na.rm = TRUE) +
  geom_text(data = Count_Values_z,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 3,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 100)
                 ) +
  labs(title = "How Often an X2 Arrives at a Given Stop",
       x = "Zip Code of Destination",
       y = "Time Between Busses (min)"
      )
TimeBtwEvents_X2_BoxPlot_z

Test investigation of just the X2 Route. Violin plots for time between bus arrivals (by Zip Code).

TimeBtwEvents_X2_ViolinPlot_z <- ggplot(select(as.data.frame(X2_ByStop),
                                               TimeToEvent_Min,
                                               Stop_Zip
                                               ),
                                        aes(factor(Stop_Zip),
                                            TimeToEvent_Min,
                                            fill = factor(Stop_Zip)
                                           )
                                       ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = Count_Values_z,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 60)
                 ) +
  labs(title = "How Often an X2 Arrives at a Given Stop",
       x = "Zip Code of Destination",
       y = "Time Between Busses (min)"
      )
TimeBtwEvents_X2_ViolinPlot_z

Waiting time analyses.

Munging and sampling data to go from time beteen buses to “average” waiting time.

First, get the max and min times of bus stops (each day, and for each route).

rm(X2, X2_ByStop, X2_Long, X2_Pct)
object 'X2_Long' not foundobject 'X2_Pct' not found
RouteMinMax <- group_by(NewTravTime,
                        Route,
                        Event_Time_Date
                       ) %>% 
  summarise(MinTime = min(Event_Time),
            MaxTime = max(Event_Time)
           )
str(RouteMinMax)
Classes ‘grouped_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 1329 obs. of  4 variables:
 $ Route          : chr  "10A" "10A" "10A" "10A" ...
 $ Event_Time_Date: int  3 4 5 6 7 3 4 5 6 7 ...
 $ MinTime        : POSIXct, format: "2016-10-03 00:00:19" "2016-10-04 00:00:54" ...
 $ MaxTime        : POSIXct, format: "2016-10-03 23:57:27" "2016-10-04 23:59:23" ...
 - attr(*, "vars")=List of 1
  ..$ : symbol Route
 - attr(*, "drop")= logi TRUE
View(RouteMinMax)
head(RouteMinMax, 50)

Waiting time analyses.

Munging and sampling data to go from time beteen buses to “average” waiting time.

(Pulls here are done by day, as the data are too large to do at once.)

# View(head(NewTravTime, 500))

# For each record, create a random datetime between the first and last stop for that bus route (on that day).
for(i in 3:7){

set.seed(123456789)
Samp <- select(NewTravTime,
               RowNum_OG,
               Route,
               # RouteGroup,
               Event_Time_Date,
               StopID_Clean,
               starts_with("Event")
              ) %>% 
  filter(Event_Time_Date == i) %>%  # needed to do this each day (3-7) because the complete file was too large to do at once
  left_join(RouteMinMax,
            by = c("Route" = "Route",
                   "Event_Time_Date" = "Event_Time_Date"
                  )
           ) %>% 
  mutate(SampTime = as_datetime(runif(nrow(.), #200000,
                                      min = MinTime,
                                      max = MaxTime
                                     ),
                                tz = "America/New_York"
                               )
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         ) 

# str(Samp)
# View(head(Samp, 500))
# 
# View(
# group_by(Samp,
#          RowNum_OG
#         ) %>%
#   summarise(Cnt_Num = n(),
#             Cnt_Pct = 100 * Cnt_Num / nrow(Samp)
#            ) %>%
#   arrange(desc(Cnt_Num))
# )


# For each Route and StopID combination, get all the Event_Time values that are after the SampTime value.
# estimating approx 2hrs of runtime for all 2.8m records
Testing_A <- sqldf("   Select               t1.*
                                            ,t2.Event_Time             as NextBus
                        From                 Samp                      as t1
                             Inner Join      Samp                      as t2
                                On              t1.Route = t2.Route
                                And             t1.StopID_Clean = t2.StopID_Clean
                                And             t2.Event_Time > t1.SampTime
                        Order By             t1.Route
                                            ,t1.StopID_Clean
                                            ,t1.Event_Time
                                            ,t2.Event_Time
                  "
                 ) %>% 
  mutate(NB = as_datetime(NextBus,
                          tz = "America/New_York"
                         )
        )

# str(Testing_A)
# View(head(Testing_A, 500))
# View(head(Samp, 500))


# Filter the dataframe to only include the bus arrival at StopID that is the next to come after the SampTime.
# estimating approx 20min of runtime for all 2.8m records
Testing <- select(Testing_A,
                  -NextBus
                 ) %>% 
  group_by(RowNum_OG) %>% 
  filter(NB == min(NB)
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         ) %>% 
  mutate(WaitTime_Min = as.numeric(NB - SampTime),
         WaitTime_Sec = WaitTime_Min * 60,
         WaitTime_Sec2 = NB - SampTime,
         WaitTime_Min2 = WaitTime_Sec2 / 60
        ) %>% 
  as.data.frame()

assign(paste0("Testing_", i),
       Testing
      )

rm(Samp,Testing_A, Testing)
str(get(paste0("Testing_", i)))
View(get(paste0("Testing_", i)))
}


# Bind all the individual dataframes together.
WaitData_DayPull <- bind_rows(Testing_3,
                              Testing_4,
                              Testing_5,
                              Testing_6,
                              Testing_7
                             ) %>% 
  mutate(WaitTime_Sec3 = NB - SampTime,
         WaitTime_Min3 = WaitTime_Sec3 / 60
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         )


# saveRDS(WaitData_DayPull, "WaitData_DayPull")
WaitData_DayPull <- readRDS("WaitData_DayPull")
str(WaitData_DayPull)
'data.frame':   2666526 obs. of  23 variables:
 $ RowNum_OG         : int  771269 510393 842137 416282 403679 478483 842251 403790 842364 403906 ...
 $ Route             : chr  "10A" "10A" "10A" "10A" ...
 $ Event_Time_Date   : int  3 3 3 3 3 3 3 3 3 3 ...
 $ StopID_Clean      : chr  "2" "2" "2" "2" ...
 $ Event_Type        : int  3 4 3 3 4 3 3 4 3 4 ...
 $ Event_Description : Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 1 3 1 1 3 1 3 ...
 $ Event_Time_Yr     : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth    : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Day    : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr     : int  0 1 5 5 6 6 7 8 9 10 ...
 $ Event_Time_HrGroup: Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 1 1 2 2 3 3 3 3 4 4 ...
 $ Event_Time_Min    : int  1 3 5 38 21 40 12 36 21 27 ...
 $ Event_Time        : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ MinTime           : POSIXct, format: "2016-10-03 00:00:19" "2016-10-03 00:00:19" ...
 $ MaxTime           : POSIXct, format: "2016-10-03 23:57:27" "2016-10-03 23:57:27" ...
 $ SampTime          : POSIXct, format: "2016-10-03 15:35:56" "2016-10-03 10:41:52" ...
 $ NB                : POSIXct, format: "2016-10-03 16:01:44" "2016-10-03 10:42:48" ...
 $ WaitTime_Min      : num  25.79 55.82 2.44 28.76 1.48 ...
 $ WaitTime_Sec      : num  1547.1 3349.2 146.6 1725.5 88.9 ...
 $ WaitTime_Sec2     : num  25.79 55.82 2.44 28.76 1.48 ...
 $ WaitTime_Min2     : num  0.4298 0.9303 0.0407 0.4793 0.0247 ...
 $ WaitTime_Sec3     :Class 'difftime'  atomic [1:2666526] 1547.1 55.8 8794.3 1725.5 5333.4 ...
  .. ..- attr(*, "units")= chr "secs"
 $ WaitTime_Min3     :Class 'difftime'  atomic [1:2666526] 25.79 0.93 146.57 28.76 88.89 ...
  .. ..- attr(*, "units")= chr "secs"
View(head(WaitData_DayPull, 500))
View(tail(WaitData_DayPull, 500))

Waiting time analyses.

Munging and sampling data to go from time beteen buses to “average” waiting time.

Basic investigation of any missing rows from data pulled by day.

DistinctRowNum_OG <- distinct(select(WaitData_DayPull,
                                     RowNum_OG
                                    )
                             )
str(DistinctRowNum_OG)
'data.frame':   2666004 obs. of  1 variable:
 $ RowNum_OG: int  771269 510393 842137 416282 403679 478483 842251 403790 842364 403906 ...
# View(
# anti_join(Samp,
#           DistinctRowNum_OG,
#           by = c("RowNum_OG" = "RowNum_OG")
#          )
# )
# The samp time is AFTER the last bus passed that StopID_Clean
# View(filter(Samp,
#             Event_Time > "2016-10-07 19:48:41" &
#               Route == "X2" &
#               StopID_Clean == 1003774
#            )
#     )
# Next Bus (NB) can be on the next morning
# View(filter(Testing7,
#             SampTime > "2016-10-06 23:58:00" &
#               SampTime < "2016-10-06 23:59:59")
#     )

Waiting time analyses.

Munging and sampling data to go from time beteen buses to “average” waiting time.

(Pulls here are done by groupings of bus routes, as the data are too large to do at once.)

First, we need to find the most common bus routes.

rm(DistinctRowNum_OG)
# View(head(NewTravTime, 500))
set.seed(123456789)
BusGroups <- group_by(NewTravTime,
                      Route
                     ) %>% 
  summarise(Cnt_Num = n(),
            Cnt_Pct = Cnt_Num / nrow(NewTravTime)
           ) %>% 
  arrange(desc(Cnt_Num)
         ) %>% 
  mutate(RowNum = row_number(),
         RandNum = runif(n = 268),
         RouteGroup = ifelse(RandNum <= 0.2,
                             1,
                      ifelse(RandNum <= 0.4,
                             2,
                      ifelse(RandNum <= 0.6,
                             3,
                      ifelse(RandNum <= 0.8,
                             4,
                             5
                            ))))
        )
str(BusGroups)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   268 obs. of  6 variables:
 $ Route     : chr  "70" "W4" "B2" "S2" ...
 $ Cnt_Num   : int  48269 47672 43173 42934 41462 38968 38566 37761 37718 36524 ...
 $ Cnt_Pct   : num  0.0172 0.017 0.0154 0.0153 0.0148 ...
 $ RowNum    : int  1 2 3 4 5 6 7 8 9 10 ...
 $ RandNum   : num  0.693 0.673 0.654 0.719 0.922 ...
 $ RouteGroup: num  4 4 4 4 5 5 2 5 2 3 ...
View(BusGroups)
summary(BusGroups)
    Route              Cnt_Num         Cnt_Pct              RowNum      
 Length:268         Min.   :    4   Min.   :1.424e-06   Min.   :  1.00  
 Class :character   1st Qu.: 2640   1st Qu.:9.396e-04   1st Qu.: 67.75  
 Mode  :character   Median : 7358   Median :2.619e-03   Median :134.50  
                    Mean   :10483   Mean   :3.731e-03   Mean   :134.50  
                    3rd Qu.:17014   3rd Qu.:6.056e-03   3rd Qu.:201.25  
                    Max.   :48269   Max.   :1.718e-02   Max.   :268.00  
    RandNum           RouteGroup   
 Min.   :0.001084   Min.   :1.000  
 1st Qu.:0.255701   1st Qu.:2.000  
 Median :0.512479   Median :3.000  
 Mean   :0.501473   Mean   :3.022  
 3rd Qu.:0.756575   3rd Qu.:4.000  
 Max.   :0.997351   Max.   :5.000  
head(BusGroups, 50)

Waiting time analyses.

Munging and sampling data to go from time beteen buses to “average” waiting time.

(Pulls here are done by groupings of bus routes, as the data are too large to do at once.)

# View(head(NewTravTime, 500))

# For each record, create a random datetime between the first and last stop for that bus route (on that day).
for(i in 1:5){
  
set.seed(123456789)
Samp <- left_join(NewTravTime,
                  BusGroups,
                  by = c("Route" = "Route")
                  ) %>% 
  select(RowNum_OG,
         Route,
         RouteGroup,
         Event_Time_Date,
         StopID_Clean,
         starts_with("Event")
        ) %>% 
  filter(RouteGroup == i) %>%  # needed to do this each RouteGroup (1-5) because the complete file was too large to do at once
  left_join(RouteMinMax,
            by = c("Route" = "Route",
                   "Event_Time_Date" = "Event_Time_Date"
                  )
           ) %>% 
  mutate(SampTime = as_datetime(runif(nrow(.), #200000,
                                      min = MinTime,
                                      max = MaxTime
                                     ),
                                tz = "America/New_York"
                               )
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         ) 

# str(Samp)
# View(head(Samp, 500))
# 
# View(
# group_by(Samp,
#          RowNum_OG
#         ) %>%
#   summarise(Cnt_Num = n(),
#             Cnt_Pct = 100 * Cnt_Num / nrow(Samp)
#            ) %>%
#   arrange(desc(Cnt_Num))
# )


# For each Route and StopID combination, get all the Event_Time values that are after the SampTime value.
# estimating approx 2hrs of runtime for all 2.8m records
Testing_A <- sqldf("   Select               t1.*
                                            ,t2.Event_Time             as NextBus
                        From                 Samp                      as t1
                             Inner Join      Samp                      as t2
                                On              t1.Route = t2.Route
                                And             t1.StopID_Clean = t2.StopID_Clean
                                And             t2.Event_Time > t1.SampTime
                        Order By             t1.Route
                                            ,t1.StopID_Clean
                                            ,t1.Event_Time
                                            ,t2.Event_Time
                  "
                 ) %>% 
  mutate(NB = as_datetime(NextBus,
                          tz = "America/New_York"
                         )
        )

# str(Testing_A)
# View(head(Testing_A, 500))
# View(head(Samp, 500))


# Filter the dataframe to only include the bus arrival at StopID that is the next to come after the SampTime.
# estimating approx 20min of runtime for all 2.8m records
Testing <- select(Testing_A,
                  -NextBus
                 ) %>% 
  group_by(RowNum_OG) %>% 
  filter(NB == min(NB)
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         ) %>% 
  mutate(WaitTime_Min = as.numeric(NB - SampTime),
         WaitTime_Sec = WaitTime_Min * 60
        ) %>% 
  as.data.frame()

assign(paste0("Testing", i),
       Testing
      )

rm(Samp,Testing_A, Testing)
str(get(paste0("Testing", i)))
View(get(paste0("Testing", i)))
}


# Bind all the individual dataframes together.
WaitData_RoutePull <- bind_rows(Testing1,
                                Testing2,
                                Testing3,
                                Testing4,
                                Testing5
                             ) %>% 
  mutate(WaitTime_Sec2 = NB - SampTime,
         WaitTime_Min2 = WaitTime_Sec2 / 60
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         )


# saveRDS(WaitData_RoutePull, "WaitData_RoutePull")
WaitData_RoutePull <- readRDS("WaitData_RoutePull")
str(WaitData_RoutePull)
'data.frame':   2780848 obs. of  22 variables:
 $ RowNum_OG         : int  771269 510393 842137 416282 403679 478483 842251 403790 842364 403906 ...
 $ Route             : chr  "10A" "10A" "10A" "10A" ...
 $ RouteGroup        : num  4 4 4 4 4 4 4 4 4 4 ...
 $ Event_Time_Date   : int  3 3 3 3 3 3 3 3 3 3 ...
 $ StopID_Clean      : chr  "2" "2" "2" "2" ...
 $ Event_Type        : int  3 4 3 3 4 3 3 4 3 4 ...
 $ Event_Description : Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 1 3 1 1 3 1 3 ...
 $ Event_Time_Yr     : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth    : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Day    : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr     : int  0 1 5 5 6 6 7 8 9 10 ...
 $ Event_Time_HrGroup: Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 1 1 2 2 3 3 3 3 4 4 ...
 $ Event_Time_Min    : int  1 3 5 38 21 40 12 36 21 27 ...
 $ Event_Time        : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ MinTime           : POSIXct, format: "2016-10-03 00:00:19" "2016-10-03 00:00:19" ...
 $ MaxTime           : POSIXct, format: "2016-10-03 23:57:27" "2016-10-03 23:57:27" ...
 $ SampTime          : POSIXct, format: "2016-10-03 08:00:38" "2016-10-03 02:41:02" ...
 $ NB                : POSIXct, format: "2016-10-03 08:36:07" "2016-10-03 05:05:41" ...
 $ WaitTime_Min      : num  35.47 2.41 22.98 4.94 7.99 ...
 $ WaitTime_Sec      : num  2128 145 1379 296 480 ...
 $ WaitTime_Sec2     :Class 'difftime'  atomic [1:2780848] 2128 8679 1379 296 480 ...
  .. ..- attr(*, "units")= chr "secs"
 $ WaitTime_Min2     :Class 'difftime'  atomic [1:2780848] 35.47 144.65 22.98 4.94 7.99 ...
  .. ..- attr(*, "units")= chr "secs"
View(head(WaitData_RoutePull, 500))
View(tail(WaitData_RoutePull, 500))

Waiting time analyses.

Munging and sampling data to go from time beteen buses to “average” waiting time.

Compare WaitData pulled by day and pulled by route.

dim(WaitData_RoutePull)
[1] 2780848      22
dim(WaitData_DayPull)
[1] 2666526      23
nrow(WaitData_RoutePull) - nrow(WaitData_DayPull)
[1] 114322
WaitData_Diff <- anti_join(WaitData_RoutePull,
                           WaitData_DayPull,
                           by = c("RowNum_OG" = "RowNum_OG"
                                 )
                          ) %>% 
  select(-WaitTime_Min,
         -WaitTime_Sec
        )
str(WaitData_Diff)
'data.frame':   130807 obs. of  20 variables:
 $ RowNum_OG         : int  2902760 2952760 2637547 1771590 2911289 1129658 1780069 1729217 1273777 2950017 ...
 $ Route             : chr  "Z8" "Z8" "Z8" "Z8" ...
 $ RouteGroup        : num  1 1 1 1 1 1 1 1 1 1 ...
 $ Event_Time_Date   : int  7 7 6 5 7 6 6 6 3 7 ...
 $ StopID_Clean      : chr  "2005465" "2005465" "2005465" "2005465" ...
 $ Event_Type        : int  3 3 4 3 3 4 3 4 4 3 ...
 $ Event_Description : Factor w/ 3 levels "Serviced Stop                                     ",..: 1 1 3 1 1 3 1 3 3 1 ...
 $ Event_Time_Yr     : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth    : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Day    : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 6 6 5 4 6 5 5 5 2 6 ...
 $ Event_Time_Hr     : int  19 10 15 17 17 21 17 8 8 18 ...
 $ Event_Time_HrGroup: Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 7 4 6 6 6 8 6 3 3 7 ...
 $ Event_Time_Min    : int  51 18 49 55 40 8 7 17 10 7 ...
 $ Event_Time        : POSIXct, format: "2016-10-07 19:51:47" "2016-10-07 10:18:57" ...
 $ MinTime           : POSIXct, format: "2016-10-07 00:00:07" "2016-10-07 00:00:07" ...
 $ MaxTime           : POSIXct, format: "2016-10-07 23:59:55" "2016-10-07 23:59:55" ...
 $ SampTime          : POSIXct, format: "2016-10-07 04:55:14" "2016-10-07 21:41:22" ...
 $ NB                : POSIXct, format: "2016-10-07 05:12:42" "2016-10-07 21:45:51" ...
 $ WaitTime_Sec2     :Class 'difftime'  atomic [1:130807] 1048 269 10822 465 1354 ...
  .. ..- attr(*, "units")= chr "secs"
 $ WaitTime_Min2     :Class 'difftime'  atomic [1:130807] 17.46 4.48 180.36 7.75 22.56 ...
  .. ..- attr(*, "units")= chr "secs"

Waiting time analyses.

Munging and sampling data to go from time beteen buses to “average” waiting time.

Compare WaitData (pulled by route) and original data (NewTravTime).

dim(NewTravTime)  # 2,809,529 rows
[1] 2809529     128
dim(WaitData_RoutePull)  # 2,780,848 rows
[1] 2780848      22
nrow(NewTravTime) - nrow(WaitData_RoutePull)  # is 28,681 rows
[1] 28681
str(select(NewTravTime,
           -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )
'data.frame':   2809529 obs. of  56 variables:
 $ RowNum_OG                      : int  1 3 4 5 6 7 9 10 11 12 ...
 $ UniqueLatLng                   : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ group                          : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID                   : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ BusDay_EventNum                : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Bus_ID                         : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                          : chr  "S80" "S80" "S80" "S80" ...
 $ RteChange2                     : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt                       : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ DirChange2                     : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 1 2 2 2 ...
 $ Route_Direction                : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence                  : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Start_ID                       : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc                     : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StopID_Clean                   : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator               : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_Desc                      : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ countryCode                    : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State                     : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County                    : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_City                      : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ Stop_Zip                       : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ Event_Type                     : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description              : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time_Yr                  : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth                 : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date                : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day                 : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr                  : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_HrGroup             : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Min                 : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time                     : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time                 : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time                     : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Dwell_Time2                    : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time                     : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Latitude                       : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude                      : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading                        : int  199 97 276 15 119 100 274 104 241 274 ...
 $ Odometer_Distance              : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Odometer_Distance_Lag1         : int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Odometer_Distance_Mi           : num  8.25 8.55 8.79 9.49 9.67 ...
 $ TravelDistance_Ft              : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi              : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs         : num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec                 : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr                  : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph                   : num  NA 6.05 23.57 100.83 3.44 ...
 $ TravelDistance_Mi_New          : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_New_Label    : Factor w/ 5 levels "TD_Mi_SS_Mean",..: 4 4 4 4 4 4 4 3 3 4 ...
 $ TravelDistance_Mi_NewHvrs      : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_NewHvrs_Label: Factor w/ 5 levels "TD_Mi_SS_Mean",..: 4 4 4 4 4 5 4 3 3 4 ...
 $ SpeedAvg_Mph_NewHvrs           : num  NA 6.05 23.57 100.83 3.44 ...
 $ TT_Sec_New                     : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TT_Sec_New_Label               : Factor w/ 4 levels "TravelTime_Sec",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ TT_Hr_New                      : num  NA 0.05 0.01028 0.00694 0.05278 ...
str(WaitData_RoutePull)
'data.frame':   2780848 obs. of  22 variables:
 $ RowNum_OG         : int  771269 510393 842137 416282 403679 478483 842251 403790 842364 403906 ...
 $ Route             : chr  "10A" "10A" "10A" "10A" ...
 $ RouteGroup        : num  4 4 4 4 4 4 4 4 4 4 ...
 $ Event_Time_Date   : int  3 3 3 3 3 3 3 3 3 3 ...
 $ StopID_Clean      : chr  "2" "2" "2" "2" ...
 $ Event_Type        : int  3 4 3 3 4 3 3 4 3 4 ...
 $ Event_Description : Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 1 3 1 1 3 1 3 ...
 $ Event_Time_Yr     : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth    : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Day    : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr     : int  0 1 5 5 6 6 7 8 9 10 ...
 $ Event_Time_HrGroup: Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 1 1 2 2 3 3 3 3 4 4 ...
 $ Event_Time_Min    : int  1 3 5 38 21 40 12 36 21 27 ...
 $ Event_Time        : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ MinTime           : POSIXct, format: "2016-10-03 00:00:19" "2016-10-03 00:00:19" ...
 $ MaxTime           : POSIXct, format: "2016-10-03 23:57:27" "2016-10-03 23:57:27" ...
 $ SampTime          : POSIXct, format: "2016-10-03 08:00:38" "2016-10-03 02:41:02" ...
 $ NB                : POSIXct, format: "2016-10-03 08:36:07" "2016-10-03 05:05:41" ...
 $ WaitTime_Min      : num  35.47 2.41 22.98 4.94 7.99 ...
 $ WaitTime_Sec      : num  2128 145 1379 296 480 ...
 $ WaitTime_Sec2     :Class 'difftime'  atomic [1:2780848] 2128 8679 1379 296 480 ...
  .. ..- attr(*, "units")= chr "secs"
 $ WaitTime_Min2     :Class 'difftime'  atomic [1:2780848] 35.47 144.65 22.98 4.94 7.99 ...
  .. ..- attr(*, "units")= chr "secs"
Compare_NTT_WD <- left_join(NewTravTime,
                            select(WaitData_RoutePull,
                                   RowNum_OG,
                                   # Route,
                                   RouteGroup,
                                   # StopID_Clean,
                                   # Event_Time,
                                   MinTime,
                                   MaxTime,
                                   SampTime,
                                   NB,
                                   WaitTime_Sec2,
                                   WaitTime_Min2
                                  ),
                            by = c("RowNum_OG" = "RowNum_OG")
                           ) %>% 
  select(-matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         )
str(Compare_NTT_WD)  # 2,810,109 rows overall  --  29,261 rows with no match
'data.frame':   2810109 obs. of  63 variables:
 $ RowNum_OG                      : int  771269 510393 842137 416282 403679 478483 842251 403790 842364 403906 ...
 $ UniqueLatLng                   : chr  "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" ...
 $ group                          : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID                   : chr  "6000273--2" "6000273--2" "6000273--2" "6000273--2" ...
 $ BusDay_EventNum                : int  2 70 55 55 55 94 164 158 272 266 ...
 $ Bus_ID                         : int  2915 2719 2950 2634 2625 2674 2950 2625 2950 2625 ...
 $ Route                          : chr  "10A" "10A" "10A" "10A" ...
 $ RteChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt                       : Factor w/ 14 levels "1","10","11",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ DirChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ Route_Direction                : Factor w/ 12 levels "","ANTICLKW",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Stop_Sequence                  : int  55 55 55 55 55 55 55 55 55 55 ...
 $ Start_ID                       : chr  "6000273" "6000273" "6000273" "6000273" ...
 $ Start_Desc                     : chr  "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" ...
 $ StopID_Clean                   : chr  "2" "2" "2" "2" ...
 $ StopID_Indicator               : Factor w/ 2 levels "ID_Bad","ID_OK": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Desc                      : chr  "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" ...
 $ countryCode                    : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State                     : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County                    : Factor w/ 11 levels "Anne Arundel",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_City                      : Factor w/ 56 levels "Accokeek","Alexandria",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ Stop_Zip                       : Factor w/ 153 levels "20001","20002",..: 132 132 132 132 132 132 132 132 132 132 ...
 $ Event_Type                     : int  3 4 3 3 4 3 3 4 3 4 ...
 $ Event_Description              : Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 1 3 1 1 3 1 3 ...
 $ Event_Time_Yr                  : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth                 : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date                : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day                 : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr                  : int  0 1 5 5 6 6 7 8 9 10 ...
 $ Event_Time_HrGroup             : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 1 1 2 2 3 3 3 3 4 4 ...
 $ Event_Time_Min                 : int  1 3 5 38 21 40 12 36 21 27 ...
 $ Event_Time                     : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Departure_Time                 : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Dwell_Time                     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Dwell_Time2                    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Delta_Time                     : int  -210 -89 -35 149 914 253 217 1267 400 900 ...
 $ Latitude                       : num  38.9 38.9 38.9 38.9 38.9 ...
 $ Longitude                      : num  -77.1 -77.1 -77.1 -77.1 -77.1 ...
 $ Heading                        : int  23 23 23 23 23 23 23 23 23 23 ...
 $ Odometer_Distance              : int  1131407 909311 87585 80914 88439 69784 211146 212739 336615 337781 ...
 $ Odometer_Distance_Lag1         : int  1131407 908412 87585 80914 85325 69784 211146 211995 336615 337065 ...
 $ Odometer_Distance_Mi           : num  214.3 172.2 16.6 15.3 16.7 ...
 $ TravelDistance_Ft              : int  NA 899 NA NA 3114 NA NA 744 NA 716 ...
 $ TravelDistance_Mi              : num  NA 0.17 NA NA 0.59 ...
 $ TravelDistance_Mi_Hvrs         : num  0.322 0.322 0.322 0.322 0.319 ...
 $ TravelTime_Sec                 : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TravelTime_Hr                  : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ SpeedAvg_Mph                   : num  NA 3.21 NA NA 11.6 ...
 $ TravelDistance_Mi_New          : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_New_Label    : Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ TravelDistance_Mi_NewHvrs      : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_NewHvrs_Label: Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ SpeedAvg_Mph_NewHvrs           : num  12.32 3.21 8.91 13.62 11.6 ...
 $ TT_Sec_New                     : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TT_Sec_New_Label               : Factor w/ 4 levels "TravelTime_Sec",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ TT_Hr_New                      : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ RouteGroup                     : num  4 4 4 4 4 4 4 4 4 4 ...
 $ MinTime                        : POSIXct, format: "2016-10-03 00:00:19" "2016-10-03 00:00:19" ...
 $ MaxTime                        : POSIXct, format: "2016-10-03 23:57:27" "2016-10-03 23:57:27" ...
 $ SampTime                       : POSIXct, format: "2016-10-03 08:00:38" "2016-10-03 02:41:02" ...
 $ NB                             : POSIXct, format: "2016-10-03 08:36:07" "2016-10-03 05:05:41" ...
 $ WaitTime_Sec2                  :Class 'difftime'  atomic [1:2810109] 2128 8679 1379 296 480 ...
  .. ..- attr(*, "units")= chr "secs"
 $ WaitTime_Min2                  :Class 'difftime'  atomic [1:2810109] 35.47 144.65 22.98 4.94 7.99 ...
  .. ..- attr(*, "units")= chr "secs"
View(head(Compare_NTT_WD, 500))
View(filter(Compare_NTT_WD,
            is.na(MinTime)
           )
    )
# View(anti_join(Samp,
#                distinct(select(WaitData_RoutePull,
#                                RowNum_OG
#                               )
#                        ),
#                by = c("RowNum_OG" = "RowNum_OG")
#               )
#     )
# The SampTime is AFTER the last bus passed that StopID_Clean
# View(filter(Samp,
#               Route == "X2" &
#               StopID_Clean == 1003774
#             # RowNum_OG = 1146723
#             # Event_Time = 2016-10-07 15:32:18
#            )
#     )

Clean up the data a bit.

rm(BusGroups, RouteMinMax, Samp, Testing1, Testing2, Testing3, Testing4, Testing5, Testing_3, Testing_4, Testing_5, Testing_6, Testing_7, WaitData_DayPull, WaitData_Diff)
str(Compare_NTT_WD)
'data.frame':   2810109 obs. of  63 variables:
 $ RowNum_OG                      : int  771269 510393 842137 416282 403679 478483 842251 403790 842364 403906 ...
 $ UniqueLatLng                   : chr  "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" ...
 $ group                          : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID                   : chr  "6000273--2" "6000273--2" "6000273--2" "6000273--2" ...
 $ BusDay_EventNum                : int  2 70 55 55 55 94 164 158 272 266 ...
 $ Bus_ID                         : int  2915 2719 2950 2634 2625 2674 2950 2625 2950 2625 ...
 $ Route                          : chr  "10A" "10A" "10A" "10A" ...
 $ RteChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt                       : Factor w/ 14 levels "1","10","11",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ DirChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ Route_Direction                : Factor w/ 12 levels "","ANTICLKW",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Stop_Sequence                  : int  55 55 55 55 55 55 55 55 55 55 ...
 $ Start_ID                       : chr  "6000273" "6000273" "6000273" "6000273" ...
 $ Start_Desc                     : chr  "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" ...
 $ StopID_Clean                   : chr  "2" "2" "2" "2" ...
 $ StopID_Indicator               : Factor w/ 2 levels "ID_Bad","ID_OK": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Desc                      : chr  "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" ...
 $ countryCode                    : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State                     : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County                    : Factor w/ 11 levels "Anne Arundel",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_City                      : Factor w/ 56 levels "Accokeek","Alexandria",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ Stop_Zip                       : Factor w/ 153 levels "20001","20002",..: 132 132 132 132 132 132 132 132 132 132 ...
 $ Event_Type                     : int  3 4 3 3 4 3 3 4 3 4 ...
 $ Event_Description              : Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 1 3 1 1 3 1 3 ...
 $ Event_Time_Yr                  : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth                 : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date                : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day                 : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr                  : int  0 1 5 5 6 6 7 8 9 10 ...
 $ Event_Time_HrGroup             : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 1 1 2 2 3 3 3 3 4 4 ...
 $ Event_Time_Min                 : int  1 3 5 38 21 40 12 36 21 27 ...
 $ Event_Time                     : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Departure_Time                 : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Dwell_Time                     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Dwell_Time2                    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Delta_Time                     : int  -210 -89 -35 149 914 253 217 1267 400 900 ...
 $ Latitude                       : num  38.9 38.9 38.9 38.9 38.9 ...
 $ Longitude                      : num  -77.1 -77.1 -77.1 -77.1 -77.1 ...
 $ Heading                        : int  23 23 23 23 23 23 23 23 23 23 ...
 $ Odometer_Distance              : int  1131407 909311 87585 80914 88439 69784 211146 212739 336615 337781 ...
 $ Odometer_Distance_Lag1         : int  1131407 908412 87585 80914 85325 69784 211146 211995 336615 337065 ...
 $ Odometer_Distance_Mi           : num  214.3 172.2 16.6 15.3 16.7 ...
 $ TravelDistance_Ft              : int  NA 899 NA NA 3114 NA NA 744 NA 716 ...
 $ TravelDistance_Mi              : num  NA 0.17 NA NA 0.59 ...
 $ TravelDistance_Mi_Hvrs         : num  0.322 0.322 0.322 0.322 0.319 ...
 $ TravelTime_Sec                 : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TravelTime_Hr                  : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ SpeedAvg_Mph                   : num  NA 3.21 NA NA 11.6 ...
 $ TravelDistance_Mi_New          : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_New_Label    : Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ TravelDistance_Mi_NewHvrs      : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_NewHvrs_Label: Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ SpeedAvg_Mph_NewHvrs           : num  12.32 3.21 8.91 13.62 11.6 ...
 $ TT_Sec_New                     : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TT_Sec_New_Label               : Factor w/ 4 levels "TravelTime_Sec",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ TT_Hr_New                      : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ RouteGroup                     : num  4 4 4 4 4 4 4 4 4 4 ...
 $ MinTime                        : POSIXct, format: "2016-10-03 00:00:19" "2016-10-03 00:00:19" ...
 $ MaxTime                        : POSIXct, format: "2016-10-03 23:57:27" "2016-10-03 23:57:27" ...
 $ SampTime                       : POSIXct, format: "2016-10-03 08:00:38" "2016-10-03 02:41:02" ...
 $ NB                             : POSIXct, format: "2016-10-03 08:36:07" "2016-10-03 05:05:41" ...
 $ WaitTime_Sec2                  :Class 'difftime'  atomic [1:2810109] 2128 8679 1379 296 480 ...
  .. ..- attr(*, "units")= chr "secs"
 $ WaitTime_Min2                  :Class 'difftime'  atomic [1:2810109] 35.47 144.65 22.98 4.94 7.99 ...
  .. ..- attr(*, "units")= chr "secs"
View(head(Compare_NTT_WD, 500))
View(head(mutate(Compare_NTT_WD,
                 WT_Min = as.numeric(WaitTime_Min2)
                )
         )
    )
WaitTime_AsNum <- Compare_NTT_WD %>% 
  mutate(RouteStop_ID = factor(paste(Route, StopID_Clean, sep = "__")
                              )
        )
WaitTime_AsNum$WaitTime_Sec2 <- as.numeric(WaitTime_AsNum$WaitTime_Sec2)
WaitTime_AsNum$WaitTime_Min2 <- as.numeric(WaitTime_AsNum$WaitTime_Min2)
rm(Compare_NTT_WD)
str(WaitTime_AsNum)
'data.frame':   2810109 obs. of  64 variables:
 $ RowNum_OG                      : int  771269 510393 842137 416282 403679 478483 842251 403790 842364 403906 ...
 $ UniqueLatLng                   : chr  "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" ...
 $ group                          : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID                   : chr  "6000273--2" "6000273--2" "6000273--2" "6000273--2" ...
 $ BusDay_EventNum                : int  2 70 55 55 55 94 164 158 272 266 ...
 $ Bus_ID                         : int  2915 2719 2950 2634 2625 2674 2950 2625 2950 2625 ...
 $ Route                          : chr  "10A" "10A" "10A" "10A" ...
 $ RteChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt                       : Factor w/ 14 levels "1","10","11",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ DirChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ Route_Direction                : Factor w/ 12 levels "","ANTICLKW",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Stop_Sequence                  : int  55 55 55 55 55 55 55 55 55 55 ...
 $ Start_ID                       : chr  "6000273" "6000273" "6000273" "6000273" ...
 $ Start_Desc                     : chr  "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" ...
 $ StopID_Clean                   : chr  "2" "2" "2" "2" ...
 $ StopID_Indicator               : Factor w/ 2 levels "ID_Bad","ID_OK": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Desc                      : chr  "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" ...
 $ countryCode                    : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State                     : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County                    : Factor w/ 11 levels "Anne Arundel",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_City                      : Factor w/ 56 levels "Accokeek","Alexandria",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ Stop_Zip                       : Factor w/ 153 levels "20001","20002",..: 132 132 132 132 132 132 132 132 132 132 ...
 $ Event_Type                     : int  3 4 3 3 4 3 3 4 3 4 ...
 $ Event_Description              : Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 1 3 1 1 3 1 3 ...
 $ Event_Time_Yr                  : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth                 : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date                : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day                 : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr                  : int  0 1 5 5 6 6 7 8 9 10 ...
 $ Event_Time_HrGroup             : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 1 1 2 2 3 3 3 3 4 4 ...
 $ Event_Time_Min                 : int  1 3 5 38 21 40 12 36 21 27 ...
 $ Event_Time                     : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Departure_Time                 : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Dwell_Time                     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Dwell_Time2                    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Delta_Time                     : int  -210 -89 -35 149 914 253 217 1267 400 900 ...
 $ Latitude                       : num  38.9 38.9 38.9 38.9 38.9 ...
 $ Longitude                      : num  -77.1 -77.1 -77.1 -77.1 -77.1 ...
 $ Heading                        : int  23 23 23 23 23 23 23 23 23 23 ...
 $ Odometer_Distance              : int  1131407 909311 87585 80914 88439 69784 211146 212739 336615 337781 ...
 $ Odometer_Distance_Lag1         : int  1131407 908412 87585 80914 85325 69784 211146 211995 336615 337065 ...
 $ Odometer_Distance_Mi           : num  214.3 172.2 16.6 15.3 16.7 ...
 $ TravelDistance_Ft              : int  NA 899 NA NA 3114 NA NA 744 NA 716 ...
 $ TravelDistance_Mi              : num  NA 0.17 NA NA 0.59 ...
 $ TravelDistance_Mi_Hvrs         : num  0.322 0.322 0.322 0.322 0.319 ...
 $ TravelTime_Sec                 : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TravelTime_Hr                  : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ SpeedAvg_Mph                   : num  NA 3.21 NA NA 11.6 ...
 $ TravelDistance_Mi_New          : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_New_Label    : Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ TravelDistance_Mi_NewHvrs      : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_NewHvrs_Label: Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ SpeedAvg_Mph_NewHvrs           : num  12.32 3.21 8.91 13.62 11.6 ...
 $ TT_Sec_New                     : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TT_Sec_New_Label               : Factor w/ 4 levels "TravelTime_Sec",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ TT_Hr_New                      : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ RouteGroup                     : num  4 4 4 4 4 4 4 4 4 4 ...
 $ MinTime                        : POSIXct, format: "2016-10-03 00:00:19" "2016-10-03 00:00:19" ...
 $ MaxTime                        : POSIXct, format: "2016-10-03 23:57:27" "2016-10-03 23:57:27" ...
 $ SampTime                       : POSIXct, format: "2016-10-03 08:00:38" "2016-10-03 02:41:02" ...
 $ NB                             : POSIXct, format: "2016-10-03 08:36:07" "2016-10-03 05:05:41" ...
 $ WaitTime_Sec2                  : num  2128 8679 1379 296 480 ...
 $ WaitTime_Min2                  : num  35.47 144.65 22.98 4.94 7.99 ...
 $ RouteStop_ID                   : Factor w/ 20897 levels "10A__2","10A__3",..: 1 1 1 1 1 1 1 1 1 1 ...

General exploration of wait times.

summary(WaitTime_AsNum$WaitTime_Min2)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
   0.000    7.863   17.550   73.270   39.390 5154.000    29261 

General exploration of wait times.

WT_Quantiles <- as.data.frame(quantile(WaitTime_AsNum$WaitTime_Min2,
                                       probs = seq(0, 1, 0.01),
                                       na.rm = TRUE
                                      )
                             )
colnames(WT_Quantiles) <- "Value_Min"
WT_Quantiles$Value_Sec = format(round(WT_Quantiles$Value_Min * 60,
                                      digits = 2
                                     ),
                                nsmall = 2
                               )
WT_Quantiles$Value_Hr = format(round(WT_Quantiles$Value_Min / 60,
                                     digits = 2
                                    ),
                                nsmall = 2
                               )
WT_Quantiles$Value_Min = format(round(WT_Quantiles$Value_Min,
                                      digits = 2
                                     ),
                                nsmall = 2
                               )
WT_Quantiles$Quantile <- seq(0, 1, 0.01)
WT_Quantiles <- select(WT_Quantiles,
                       Quantile,
                       Value_Sec,
                       Value_Min,
                       Value_Hr
                      )
str(WT_Quantiles)
'data.frame':   101 obs. of  4 variables:
 $ Quantile : num  0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 ...
 $ Value_Sec: chr  "     0.00" "    17.52" "    35.15" "    53.05" ...
 $ Value_Min: chr  "   0.00" "   0.29" "   0.59" "   0.88" ...
 $ Value_Hr : chr  " 0.00" " 0.00" " 0.01" " 0.01" ...
View(WT_Quantiles)
WT_Quantiles
View(arrange(WaitTime_AsNum,
             desc(WaitTime_Min2)
            ) %>% 
       head(., 5000)
    )
View(filter(WaitTime_AsNum,
            between(WaitTime_Min2, 60, 200)
           ) %>% 
       arrange(desc(WaitTime_Min2)
              ) 
     # %>% 
     #   head(., 5000)
    )
# Example of extreme wait times
View(filter(WaitTime_AsNum,
            Route == "W13" &  # only 2 bus passes in the entire dataset
              StopID_Clean == 1003728
            # Event_Time = 2016-10-03 08:42:46
           )
    )
# Example of extreme wait times
View(filter(WaitTime_AsNum,
            Route == "S41" &  # only 4 bus passes in the entire dataset
              StopID_Clean == 1001095
            # Event_Time = 2016-10-05 15:41:47
           )
    )
# Example of extreme wait times
View(filter(WaitTime_AsNum,
            Route == "D8" &  # route has VERY limited service after midnight
              StopID_Clean == 1001669
            # Event_Time = 2016-10-06 20:31:16
           )
    )

Looks like there might be an issue in wait times when very few Route-Stop combinations are included in the dataset. Let’s explore these.

RouteStop_Cnts <- group_by(WaitTime_AsNum,
                           RouteStop_ID
                          ) %>% 
  summarise(RouteStop_CntNum = n(),
            RouteStop_CntPct = RouteStop_CntNum / nrow(WaitTime_AsNum)
           ) %>% 
  arrange(RouteStop_CntNum)
View(RouteStop_Cnts)
RouteStop_CntOfCnt <- group_by(RouteStop_Cnts,
                               RouteStop_CntNum
                              ) %>% 
  summarise(RouteStopCnt_CntNum = n(),
            RouteStopCnt_CntPct = RouteStopCnt_CntNum / nrow(RouteStop_Cnts)
           ) %>% 
  mutate(RouteStopCnt_CntPct_CumSum = cumsum(RouteStopCnt_CntPct),
         x = 1 - RouteStopCnt_CntPct_CumSum
        ) %>% 
  arrange(RouteStop_CntNum)
  
 View(RouteStop_CntOfCnt)
 RouteStop_CntOfCnt

Histogram of the counts of Route-StopID combinations.

RouteStop_Cnts_Bar <- ggplot(RouteStop_CntOfCnt,
                             aes(x = RouteStop_CntNum,
                                 # y = ..density..
                                 y = RouteStopCnt_CntNum
                                )
                            ) +
  # geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_col(fill = "lightblue", colour = "grey60", size = 0.2) +
  coord_cartesian(xlim = c(0, 500)
                  # ylim = c(0, 0.02)
                 ) +
  labs(title = "Variation in Routes Passing a Specific Stop",
       x = "Occurrences of Route-StopID Combiantions",
       y = "Counts"
      )
RouteStop_Cnts_Bar

Create a new dataset limiting extremely small counts of Route-StopID combinations.

WaitTime_RteCnts <- left_join(WaitTime_AsNum,
                              RouteStop_Cnts,
                              by = c("RouteStop_ID" = "RouteStop_ID")
                             ) %>% 
  select(-RouteStop_CntPct)
dim(WaitTime_AsNum)
[1] 2810109      64
dim(WaitTime_RteCnts)
[1] 2810109      65
rm(WaitTime_AsNum)
str(WaitTime_RteCnts)
'data.frame':   2810109 obs. of  65 variables:
 $ RowNum_OG                      : int  771269 510393 842137 416282 403679 478483 842251 403790 842364 403906 ...
 $ UniqueLatLng                   : chr  "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" ...
 $ group                          : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID                   : chr  "6000273--2" "6000273--2" "6000273--2" "6000273--2" ...
 $ BusDay_EventNum                : int  2 70 55 55 55 94 164 158 272 266 ...
 $ Bus_ID                         : int  2915 2719 2950 2634 2625 2674 2950 2625 2950 2625 ...
 $ Route                          : chr  "10A" "10A" "10A" "10A" ...
 $ RteChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt                       : Factor w/ 14 levels "1","10","11",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ DirChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ Route_Direction                : Factor w/ 12 levels "","ANTICLKW",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Stop_Sequence                  : int  55 55 55 55 55 55 55 55 55 55 ...
 $ Start_ID                       : chr  "6000273" "6000273" "6000273" "6000273" ...
 $ Start_Desc                     : chr  "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" ...
 $ StopID_Clean                   : chr  "2" "2" "2" "2" ...
 $ StopID_Indicator               : Factor w/ 2 levels "ID_Bad","ID_OK": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Desc                      : chr  "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" ...
 $ countryCode                    : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State                     : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County                    : Factor w/ 11 levels "Anne Arundel",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_City                      : Factor w/ 56 levels "Accokeek","Alexandria",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ Stop_Zip                       : Factor w/ 153 levels "20001","20002",..: 132 132 132 132 132 132 132 132 132 132 ...
 $ Event_Type                     : int  3 4 3 3 4 3 3 4 3 4 ...
 $ Event_Description              : Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 1 3 1 1 3 1 3 ...
 $ Event_Time_Yr                  : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth                 : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date                : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day                 : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr                  : int  0 1 5 5 6 6 7 8 9 10 ...
 $ Event_Time_HrGroup             : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 1 1 2 2 3 3 3 3 4 4 ...
 $ Event_Time_Min                 : int  1 3 5 38 21 40 12 36 21 27 ...
 $ Event_Time                     : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Departure_Time                 : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Dwell_Time                     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Dwell_Time2                    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Delta_Time                     : int  -210 -89 -35 149 914 253 217 1267 400 900 ...
 $ Latitude                       : num  38.9 38.9 38.9 38.9 38.9 ...
 $ Longitude                      : num  -77.1 -77.1 -77.1 -77.1 -77.1 ...
 $ Heading                        : int  23 23 23 23 23 23 23 23 23 23 ...
 $ Odometer_Distance              : int  1131407 909311 87585 80914 88439 69784 211146 212739 336615 337781 ...
 $ Odometer_Distance_Lag1         : int  1131407 908412 87585 80914 85325 69784 211146 211995 336615 337065 ...
 $ Odometer_Distance_Mi           : num  214.3 172.2 16.6 15.3 16.7 ...
 $ TravelDistance_Ft              : int  NA 899 NA NA 3114 NA NA 744 NA 716 ...
 $ TravelDistance_Mi              : num  NA 0.17 NA NA 0.59 ...
 $ TravelDistance_Mi_Hvrs         : num  0.322 0.322 0.322 0.322 0.319 ...
 $ TravelTime_Sec                 : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TravelTime_Hr                  : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ SpeedAvg_Mph                   : num  NA 3.21 NA NA 11.6 ...
 $ TravelDistance_Mi_New          : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_New_Label    : Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ TravelDistance_Mi_NewHvrs      : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_NewHvrs_Label: Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ SpeedAvg_Mph_NewHvrs           : num  12.32 3.21 8.91 13.62 11.6 ...
 $ TT_Sec_New                     : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TT_Sec_New_Label               : Factor w/ 4 levels "TravelTime_Sec",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ TT_Hr_New                      : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ RouteGroup                     : num  4 4 4 4 4 4 4 4 4 4 ...
 $ MinTime                        : POSIXct, format: "2016-10-03 00:00:19" "2016-10-03 00:00:19" ...
 $ MaxTime                        : POSIXct, format: "2016-10-03 23:57:27" "2016-10-03 23:57:27" ...
 $ SampTime                       : POSIXct, format: "2016-10-03 08:00:38" "2016-10-03 02:41:02" ...
 $ NB                             : POSIXct, format: "2016-10-03 08:36:07" "2016-10-03 05:05:41" ...
 $ WaitTime_Sec2                  : num  2128 8679 1379 296 480 ...
 $ WaitTime_Min2                  : num  35.47 144.65 22.98 4.94 7.99 ...
 $ RouteStop_ID                   : Factor w/ 20897 levels "10A__2","10A__3",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ RouteStop_CntNum               : int  175 175 175 175 175 175 175 175 175 175 ...
# Total rows
nrow(WaitTime_RteCnts)
[1] 2810109
# Rows of rare RouteStops
nrow(filter(WaitTime_RteCnts,
            RouteStop_CntNum <= 60
           )
    ) / nrow(WaitTime_RteCnts)
[1] 0.07346156
# Rows of extremely long wait times
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 180
           )
    )
[1] 298242
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 60
           )
    ) / nrow(WaitTime_RteCnts)
[1] 0.1854465
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 120
           )
    ) / nrow(WaitTime_RteCnts)
[1] 0.1366438
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 180
           )
    ) / nrow(WaitTime_RteCnts)
[1] 0.1061318
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 240
           )
    ) / nrow(WaitTime_RteCnts)
[1] 0.08077089
message("All records")
All records
select(WaitTime_RteCnts,
       WaitTime_Min2
      ) %>% 
  summary()
 WaitTime_Min2     
 Min.   :   0.000  
 1st Qu.:   7.863  
 Median :  17.554  
 Mean   :  73.268  
 3rd Qu.:  39.394  
 Max.   :5154.346  
 NA's   :29261     
message("12 passes per day in a 5-day dataset")
12 passes per day in a 5-day dataset
filter(WaitTime_RteCnts,
       RouteStop_CntNum > 60  # 12 passes per day in a 5-day dataset
      ) %>% 
  select(WaitTime_Min2) %>% 
  summary()
 WaitTime_Min2     
 Min.   :   0.000  
 1st Qu.:   7.477  
 Median :  16.512  
 Mean   :  49.070  
 3rd Qu.:  33.572  
 Max.   :2478.582  
 NA's   :16298     
message("<180min. >=180min, probably means something went wrong")
<180min. >=180min, probably means something went wrong
filter(WaitTime_RteCnts,
       WaitTime_Min2 < 180  # probably means that something went wrong
      ) %>% 
  select(WaitTime_Min2) %>% 
  summary()
 WaitTime_Min2    
 Min.   :  0.000  
 1st Qu.:  6.969  
 Median : 15.156  
 Mean   : 25.022  
 3rd Qu.: 28.187  
 Max.   :180.000  

Compare quantiles in the limited datasets.

a <- as.data.frame(select(WaitTime_RteCnts,
                          WaitTime_Min2
                         ) %>% 
                     quantile(probs = seq(0, 1, 0.01), na.rm = TRUE)
                  )
b <- as.data.frame(filter(WaitTime_RteCnts,
                          RouteStop_CntNum > 60
                         ) %>% 
                     select(WaitTime_Min2) %>% 
                     quantile(probs = seq(0, 1, 0.01), na.rm = TRUE)
                  )
c <- as.data.frame(filter(WaitTime_RteCnts,
                          WaitTime_Min2 < 180
                         ) %>% 
                     select(WaitTime_Min2) %>% 
                     quantile(probs = seq(0, 1, 0.01), na.rm = TRUE)
                  )
WT_Filter_Quantiles <- bind_cols(a, b, c) %>% 
  mutate(Quantile = seq(0, 1, 0.01)
        )
colnames(WT_Filter_Quantiles) <- c("All", "RteStpAbv60", "WTBlw180", "Quantile")
rm(a, b, c)
View(WT_Filter_Quantiles)
WT_Filter_Quantiles

Histogram of all wait times.

WaitTime_AllBus_HistDen <- ggplot(filter(select(WaitTime_RteCnts,
                                                WaitTime_Min2
                                               ),
                                         !is.na(WaitTime_Min2)
                                        ),
                                  aes(x = WaitTime_Min2,
                                      y = ..density..
                                     )
                                ) +
  geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  scale_x_continuous(breaks = seq(0, 300, 30)
                    ) +
  coord_cartesian(xlim = c(0, 300),
                  ylim = c(0, 0.035)
                 ) +
  labs(title = "Variation in Wait Time",
       x = "Wait Time (min)",
       y = "Density"
      )
WaitTime_AllBus_HistDen

Box plots for WaitTime (all busses, by Zip Code).

# Count_Values is needed to display the medians on the box plots
BusRoute <- select(WaitTime_RteCnts,
                   Route,
                   WaitTime_Min2,
                   Stop_Zip
                  ) %>% 
  filter(Route == "X2")
CountValues_AllBus_Zip <- ddply(BusRoute,
                                .(Stop_Zip),
                                summarise,
                                Value_Counts = median(WaitTime_Min2, na.rm = TRUE)
                               )
WaitTime_AllBus_Zip_Box <- ggplot(BusRoute,
                                  aes(factor(Stop_Zip),
                                      WaitTime_Min2,
                                      fill = factor(Stop_Zip)
                                     )
                                 ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE, na.rm = TRUE) +
  geom_text(data = CountValues_AllBus_Zip,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 3,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 45)
                 ) +
  labs(title = "Waiting Time at a Given Stop (for the X2)",
       x = "Zip Code of Destination",
       y = "Waiting Time (min)"
      )
WaitTime_AllBus_Zip_Box

Test investigation of just the X2 Route. Violin plots for time between bus arrivals (by Zip Code).

WaitTime_AllBus_Zip_Violin <- ggplot(BusRoute,
                                     aes(factor(Stop_Zip),
                                         WaitTime_Min2,
                                         fill = factor(Stop_Zip)
                                        )
                                    ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = CountValues_AllBus_Zip,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 3.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 45)
                 ) +
  labs(title = "Waiting Time at a Given Stop (for the X2)",
       x = "Zip Code of Destination",
       y = "Waiting Time (min)"
      )
TimeBtwEvents_X2_ViolinPlot_z

Box plots for WaitTime (Zip Code, by HourGroupZip).

# Count_Values is needed to display the medians on the box plots
Zip <- select(WaitTime_RteCnts,
              Route,
              WaitTime_Min2,
              Stop_Zip,
              Event_Time_HrGroup
             ) %>% 
  filter(Stop_Zip == 20002)
CountValues_AllBus_HG <- ddply(Zip,
                               .(Event_Time_HrGroup),
                               summarise,
                               Value_Counts = median(WaitTime_Min2,
                                                     na.rm = TRUE
                                                    )
                               )
WaitTime_AllBus_HG_Box <- ggplot(Zip,
                                 aes(factor(Event_Time_HrGroup),
                                     WaitTime_Min2,
                                     fill = factor(Event_Time_HrGroup)
                                    )
                                ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE, na.rm = TRUE) +
  geom_text(data = CountValues_AllBus_HG,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 45)
                 ) +
  labs(title = "Waiting Time at a Given Stop (for Zip 20002)",
       x = "Hour Group",
       y = "Waiting Time (min)"
      )
  # facet_wrap(~Stop_Zip
  #            # nrow = 5
  #           )
WaitTime_AllBus_HG_Box

Violin plots for WaitTime (Zip Code, by HourGroupZip).

WaitTime_AllBus_HG_Vln <- ggplot(Zip,
                                 aes(factor(Event_Time_HrGroup),
                                     WaitTime_Min2,
                                     fill = factor(Event_Time_HrGroup)
                                    )
                                ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = CountValues_AllBus_HG,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 90)
                 ) +
  labs(title = "Waiting Time at a Given Stop (for Zip 20002)",
       x = "Hour Group",
       y = "Waiting Time (min)"
      )
  # facet_wrap(~Stop_Zip
  #            # nrow = 5
  #           )
WaitTime_AllBus_HG_Vln

Box plots for WaitTime (Route, by HourGroupZip).

# Count_Values is needed to display the medians on the box plots
Rte <- select(WaitTime_RteCnts,
              Route,
              WaitTime_Min2,
              Stop_Zip,
              Event_Time_HrGroup
             ) %>% 
  filter(Route == "X2")
CountValues_AllBus_RteHG <- group_by(Rte,
                                     Event_Time_HrGroup
                                    ) %>% 
  summarise(
    Value_Counts = median(WaitTime_Min2,
                          na.rm = TRUE
                         ),
    VC = quantile(WaitTime_Min2, probs = 0.9, na.rm = TRUE)
    )
WaitTime_AllBus_RteHG_Box <- ggplot(Rte,
                                    aes(factor(Event_Time_HrGroup),
                                        WaitTime_Min2,
                                        fill = factor(Event_Time_HrGroup)
                                       )
                                   ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE, na.rm = TRUE) +
  geom_text(data = CountValues_AllBus_RteHG,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, max(CountValues_AllBus_RteHG$VC))
                 ) +
  labs(title = "Waiting Time at a Given Stop",
       subtitle = ("Route X2"),
       x = "Hour Group",
       y = "Waiting Time (min)"
      ) 
# +
#   facet_wrap(~Stop_Zip
#              # nrow = 5
#             )
WaitTime_AllBus_RteHG_Box

Violin plots for WaitTime (Zip Code, by HourGroupZip).

WaitTime_AllBus_RteHG_Vln <- ggplot(Rte,
                                    aes(factor(Event_Time_HrGroup),
                                        WaitTime_Min2,
                                        fill = factor(Event_Time_HrGroup)
                                       )
                                   ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = CountValues_AllBus_RteHG,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 45)
                 ) +
  labs(title = "Waiting Time at a Given Stop",
       subtitle = ("(Route X2)"),
       x = "Hour Group",
       y = "Waiting Time (min)"
      ) +
  facet_wrap(~Stop_Zip
             # nrow = 5
            )
WaitTime_AllBus_RteHG_Vln

X2 Percentiles Line Graph Test.

X2_Pct <- select(WaitTime_RteCnts,
                 Route,
                 Stop_Zip,
                 Event_Time_Date,
                 Event_Time_Day,
                 Event_Time_HrGroup,
                 Event_Time_Hr,
                 Latitude,
                 Longitude,
                 WaitTime_Min2
                ) %>% 
  filter(Route == "X2") %>% 
  group_by(Event_Time_Hr,
           Stop_Zip
          ) %>% 
  summarise(Pct50 = quantile(WaitTime_Min2, probs = 0.5, na.rm = TRUE),
            Pct60 = quantile(WaitTime_Min2, probs = 0.6, na.rm = TRUE),
            Pct70 = quantile(WaitTime_Min2, probs = 0.7, na.rm = TRUE),
            Pct80 = quantile(WaitTime_Min2, probs = 0.8, na.rm = TRUE),
            Pct90 = quantile(WaitTime_Min2, probs = 0.9, na.rm = TRUE)
           )
str(X2_Pct)
Classes ‘grouped_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 120 obs. of  7 variables:
 $ Event_Time_Hr: int  0 0 0 0 0 1 1 1 1 1 ...
 $ Stop_Zip     : Factor w/ 153 levels "20001","20002",..: 1 2 5 6 17 1 2 5 6 17 ...
 $ Pct50        : num  8.27 7.56 9.59 11.47 6.19 ...
 $ Pct60        : num  11.12 10.41 12.29 12.28 9.75 ...
 $ Pct70        : num  14 13 14.8 18.2 11.9 ...
 $ Pct80        : num  17.4 17.4 19.4 23.6 14.8 ...
 $ Pct90        : num  23.1 26.7 32.5 41 25.4 ...
 - attr(*, "vars")=List of 1
  ..$ : symbol Event_Time_Hr
 - attr(*, "drop")= logi TRUE
View(X2_Pct)
X2_Long <- gather(X2_Pct,
                  key = Percentile,
                  value = Pctile,
                  Pct50,
                  Pct60,
                  Pct70,
                  Pct80,
                  Pct90
                )
str(X2_Long)
Classes ‘grouped_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 600 obs. of  4 variables:
 $ Event_Time_Hr: int  0 0 0 0 0 1 1 1 1 1 ...
 $ Stop_Zip     : Factor w/ 153 levels "20001","20002",..: 1 2 5 6 17 1 2 5 6 17 ...
 $ Percentile   : chr  "Pct50" "Pct50" "Pct50" "Pct50" ...
 $ Pctile       : num  8.27 7.56 9.59 11.47 6.19 ...
 - attr(*, "vars")=List of 1
  ..$ : symbol Event_Time_Hr
 - attr(*, "drop")= logi TRUE
 - attr(*, "indices")=List of 24
  ..$ : int  0 1 2 3 4 120 121 122 123 124 ...
  ..$ : int  5 6 7 8 9 125 126 127 128 129 ...
  ..$ : int  10 11 12 13 14 130 131 132 133 134 ...
  ..$ : int  15 16 17 18 19 135 136 137 138 139 ...
  ..$ : int  20 21 22 23 24 140 141 142 143 144 ...
  ..$ : int  25 26 27 28 29 145 146 147 148 149 ...
  ..$ : int  30 31 32 33 34 150 151 152 153 154 ...
  ..$ : int  35 36 37 38 39 155 156 157 158 159 ...
  ..$ : int  40 41 42 43 44 160 161 162 163 164 ...
  ..$ : int  45 46 47 48 49 165 166 167 168 169 ...
  ..$ : int  50 51 52 53 54 170 171 172 173 174 ...
  ..$ : int  55 56 57 58 59 175 176 177 178 179 ...
  ..$ : int  60 61 62 63 64 180 181 182 183 184 ...
  ..$ : int  65 66 67 68 69 185 186 187 188 189 ...
  ..$ : int  70 71 72 73 74 190 191 192 193 194 ...
  ..$ : int  75 76 77 78 79 195 196 197 198 199 ...
  ..$ : int  80 81 82 83 84 200 201 202 203 204 ...
  ..$ : int  85 86 87 88 89 205 206 207 208 209 ...
  ..$ : int  90 91 92 93 94 210 211 212 213 214 ...
  ..$ : int  95 96 97 98 99 215 216 217 218 219 ...
  ..$ : int  100 101 102 103 104 220 221 222 223 224 ...
  ..$ : int  105 106 107 108 109 225 226 227 228 229 ...
  ..$ : int  110 111 112 113 114 230 231 232 233 234 ...
  ..$ : int  115 116 117 118 119 235 236 237 238 239 ...
 - attr(*, "group_sizes")= int  25 25 25 25 25 25 25 25 25 25 ...
 - attr(*, "biggest_group_size")= int 25
 - attr(*, "labels")='data.frame':  24 obs. of  1 variable:
  ..$ Event_Time_Hr: int  0 1 2 3 4 5 6 7 8 9 ...
  ..- attr(*, "vars")=List of 1
  .. ..$ : symbol Event_Time_Hr
  ..- attr(*, "drop")= logi TRUE
View(X2_Long)
X2_WaitByHr_Line <- ggplot(X2_Long,
                           aes(x = Event_Time_Hr,
                               y = Pctile,
                               factor(Percentile),
                               color = Percentile
                              )
                          ) +
  geom_line() +
  theme(legend.title=element_blank(),
        legend.position = "bottom"
       ) +
  coord_cartesian(xlim = c(0, 23)
                  # ylim = c(0, 45)
                 ) + 
  scale_x_continuous(breaks = seq(0, 23, 2)
                    ) +
  labs(title = "Waiting Time Throughout the Day",
       subtitle = ("(Route X2)"),
       x = "Hour of the Day",
       y = "Waiting Time (min)"
      ) +
  facet_wrap(~Stop_Zip)
X2_WaitByHr_Line

GET DATA READY FOR SHINY – GET DATA READY FOR SHINY – GET DATA READY FOR SHINY GET DATA READY FOR SHINY – GET DATA READY FOR SHINY – GET DATA READY FOR SHINY GET DATA READY FOR SHINY – GET DATA READY FOR SHINY – GET DATA READY FOR SHINY

BaseData: Used in plots by hour and zipcode (first two Shiny tabs).

# str(WaitTime_RteCnts)
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 60
           )
    ) / nrow(WaitTime_RteCnts)
[1] 0.8041407
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 120
           )
    ) / nrow(WaitTime_RteCnts)
[1] 0.8529434
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 180
           )
    )
[1] 2482606
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 180
           )
    ) / nrow(WaitTime_RteCnts)
[1] 0.8834554
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 240
           )
    ) / nrow(WaitTime_RteCnts)
[1] 0.9088163
Shiny_WaitData_Base <- select(WaitTime_RteCnts,
                              Route,
                              Stop_Zip,
                              Event_Time,
                              Event_Time_Date,
                              Event_Time_Day,
                              Event_Time_HrGroup,
                              Event_Time_Hr,
                              Latitude,
                              Longitude,
                              WaitTime_Min2
                             ) %>% 
  mutate(Event_Time_YrMthDayHr = floor_date(Event_Time, "hour")
        ) %>% 
  rename(ZipCode = Stop_Zip,
         HourGroup = Event_Time_HrGroup,
         Date = Event_Time_Date,
         Day = Event_Time_Day,
         Hour = Event_Time_Hr,
         WaitTime_Min = WaitTime_Min2
        ) %>% 
  filter(WaitTime_Min <= 180)
Shiny_WaitData_Base$Route <- factor(Shiny_WaitData_Base$Route)
str(Shiny_WaitData_Base)
'data.frame':   2482606 obs. of  11 variables:
 $ Route                : Factor w/ 268 levels "10A","10B","10E",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ ZipCode              : Factor w/ 153 levels "20001","20002",..: 132 132 132 132 132 132 132 132 132 132 ...
 $ Event_Time           : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Date                 : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Day                  : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ HourGroup            : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 1 1 2 2 3 3 3 3 4 4 ...
 $ Hour                 : int  0 1 5 5 6 6 7 8 9 10 ...
 $ Latitude             : num  38.9 38.9 38.9 38.9 38.9 ...
 $ Longitude            : num  -77.1 -77.1 -77.1 -77.1 -77.1 ...
 $ WaitTime_Min         : num  35.47 144.65 22.98 4.94 7.99 ...
 $ Event_Time_YrMthDayHr: POSIXct, format: "2016-10-03 00:00:00" "2016-10-03 01:00:00" ...
View(tail(Shiny_WaitData_Base, 500))
saveRDS(Shiny_WaitData_Base,
        "Shiny_WaitData_Base"
       )
# Shiny_WaitData_Base <- readRDS("Shiny_WaitData_Base")

Prep data for mapping.

Load and prep the Zip Code shapefile. The shapefile was originally obtained from data.gov.

# devtools::install_github("dkahle/ggmap")
# devtools::install_github("hadley/ggplot2")
# install.packages("ggmap", type = "source")
# devtools::install_github('hadley/ggplot2')
# devtools::install_github("hadley/ggplot2@v2.2.0")
# devtools::install_github('thomasp85/ggforce')
# devtools::install_github('thomasp85/ggraph')
# devtools::install_github('slowkow/ggrepel')
# shapefile originally obtained from: https://catalog.data.gov/dataset/tiger-line-shapefile-2016-2010-nation-u-s-2010-census-5-digit-zip-code-tabulation-area-zcta5-na
tract <- 
  readOGR(dsn = paste0(BasePath, "DCMetroBus/tl_2016_us_zcta510"),
          layer = "tl_2016_us_zcta510"
         )
OGR data source with driver: ESRI Shapefile 
Source: "/Users/mdturse/Desktop/Analytics/DCMetroBus/tl_2016_us_zcta510", layer: "tl_2016_us_zcta510"
with 33144 features
It has 9 fields
Integer64 fields read as strings:  ALAND10 AWATER10 
  
class(tract)
[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"
# convert the GEOID to a character
tract@data$GEOID <- as.character(tract@data$GEOID)
str(tract@data)
'data.frame':   33144 obs. of  10 variables:
 $ ZCTA5CE10 : Factor w/ 33144 levels "00601","00602",..: 13960 13961 13962 13963 13964 13965 13966 13967 13968 13969 ...
 $ GEOID10   : Factor w/ 33144 levels "00601","00602",..: 13960 13961 13962 13963 13964 13965 13966 13967 13968 13969 ...
 $ CLASSFP10 : Factor w/ 1 level "B5": 1 1 1 1 1 1 1 1 1 1 ...
 $ MTFCC10   : Factor w/ 1 level "G6350": 1 1 1 1 1 1 1 1 1 1 ...
 $ FUNCSTAT10: Factor w/ 1 level "S": 1 1 1 1 1 1 1 1 1 1 ...
 $ ALAND10   : Factor w/ 33140 levels "100004777","1000159",..: 26688 3012 32167 22975 14326 28317 27333 10155 30858 15904 ...
 $ AWATER10  : Factor w/ 28394 levels "0","10000081",..: 5631 3593 28385 1 17213 25543 23302 1 2479 233 ...
 $ INTPTLAT10: Factor w/ 33140 levels "-14.3233846",..: 22511 23065 23452 22365 23112 23329 22400 23041 22748 23214 ...
 $ INTPTLON10: Factor w/ 33144 levels "-064.6829328",..: 12183 11482 11324 11978 11755 12112 12293 12059 11417 12055 ...
 $ GEOID     : chr  "43451" "43452" "43456" "43457" ...
ggtract <- tidy(tract, region = "GEOID")
saveRDS(ggtract, "ggtract")
# ggtract <- readRDS("ggtract")
str(ggtract)
'data.frame':   52669641 obs. of  7 variables:
 $ long : num  -66.7 -66.7 -66.7 -66.7 -66.7 ...
 $ lat  : num  18.2 18.2 18.2 18.2 18.2 ...
 $ order: int  1 2 3 4 5 6 7 8 9 10 ...
 $ hole : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ piece: Factor w/ 42 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ group: Factor w/ 43395 levels "00601.1","00602.1",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ id   : chr  "00601" "00601" "00601" "00601" ...
summary(ggtract)
      long              lat             order             hole         
 Min.   :-176.68   Min.   :-14.37   Min.   :       1   Mode :logical   
 1st Qu.:-104.53   1st Qu.: 34.95   1st Qu.:13167411   FALSE:52207387  
 Median : -90.70   Median : 38.58   Median :26334821   TRUE :462254    
 Mean   : -94.14   Mean   : 38.73   Mean   :26334821   NA's :0         
 3rd Qu.: -82.09   3rd Qu.: 42.25   3rd Qu.:39502231                   
 Max.   : 145.83   Max.   : 71.34   Max.   :52669641                   
                                                                       
     piece              group               id           
 1      :50798957   99921.1:   48097   Length:52669641   
 2      : 1536244   04478.1:   27888   Class :character  
 3      :  222570   84535.1:   27236   Mode  :character  
 4      :   60093   83611.1:   26758                     
 5      :   19486   84532.1:   26309                     
 6      :    9214   79735.1:   25998                     
 (Other):   23077   (Other):52487355                     
# View(head(ggtract, 50))

Prep data for mapping.

Join the mapping data to the base data used in Shiny.

ZipWaitTest <- filter(Shiny_WaitData_Base,
                      WaitTime_Min <= 180 &
                        !is.na(ZipCode)
                     ) %>% 
  group_by(ZipCode,
           Event_Time_YrMthDayHr
           # Event_Time_Day,
           # Event_Time_Hr
          ) %>% 
  summarise(Pct80 = quantile(WaitTime_Min, probs = 0.8, na.rm = TRUE)
           ) %>% 
  arrange(# Event_Time_Hr,
          ZipCode,
          Event_Time_YrMthDayHr
         ) %>% 
  as.data.frame() %>% 
  mutate(Event_Time_DateNew = floor_date(Event_Time_YrMthDayHr, "day"),
         Event_Time_HrNew = hour(Event_Time_YrMthDayHr),
         Pct80_Level = factor(ifelse(Pct80 < 10,
                                     "Below 10",
                              ifelse(Pct80 < 20,
                                     "Below 20",
                              ifelse(Pct80 < 30,
                                     "Below 30",
                              ifelse(Pct80 < 40,
                                     "Below 40",
                              ifelse(Pct80 < 50,
                                     "Below 50",
                              ifelse(Pct80 < 60,
                                     "Below 60",
                                     "60 Plus"
                                    )))))),
                              levels = c("Below 10", "Below 20", "Below 30", 
                                         "Below 40", "Below 50", "Below 60", "60 Plus"
                                        ),
                              ordered = TRUE
                             )
        )
str(ZipWaitTest)
'data.frame':   14666 obs. of  6 variables:
 $ ZipCode              : Factor w/ 153 levels "20001","20002",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Event_Time_YrMthDayHr: POSIXct, format: "2016-10-03 00:00:00" "2016-10-03 01:00:00" ...
 $ Pct80                : num  26.9 20.5 22.8 25.5 29.3 ...
 $ Event_Time_DateNew   : POSIXct, format: "2016-10-03" "2016-10-03" ...
 $ Event_Time_HrNew     : int  0 1 2 4 5 6 7 8 9 10 ...
 $ Pct80_Level          : Ord.factor w/ 7 levels "Below 10"<"Below 20"<..: 3 3 3 3 3 3 3 3 3 3 ...
ZipWaitTest$ZipCode <- as.character(ZipWaitTest$ZipCode)
str(ZipWaitTest)
'data.frame':   14666 obs. of  6 variables:
 $ ZipCode              : chr  "20001" "20001" "20001" "20001" ...
 $ Event_Time_YrMthDayHr: POSIXct, format: "2016-10-03 00:00:00" "2016-10-03 01:00:00" ...
 $ Pct80                : num  26.9 20.5 22.8 25.5 29.3 ...
 $ Event_Time_DateNew   : POSIXct, format: "2016-10-03" "2016-10-03" ...
 $ Event_Time_HrNew     : int  0 1 2 4 5 6 7 8 9 10 ...
 $ Pct80_Level          : Ord.factor w/ 7 levels "Below 10"<"Below 20"<..: 3 3 3 3 3 3 3 3 3 3 ...
summary(ZipWaitTest)
   ZipCode          Event_Time_YrMthDayHr             Pct80         
 Length:14666       Min.   :2016-10-03 00:00:00   Min.   :  0.1644  
 Class :character   1st Qu.:2016-10-04 08:00:00   1st Qu.: 26.9316  
 Mode  :character   Median :2016-10-05 13:00:00   Median : 32.6174  
                    Mean   :2016-10-05 12:42:39   Mean   : 38.5860  
                    3rd Qu.:2016-10-06 18:00:00   3rd Qu.: 43.3970  
                    Max.   :2016-10-07 23:00:00   Max.   :177.7933  
                                                                    
 Event_Time_DateNew            Event_Time_HrNew   Pct80_Level  
 Min.   :2016-10-03 00:00:00   Min.   : 0.00    Below 10: 216  
 1st Qu.:2016-10-04 00:00:00   1st Qu.: 7.00    Below 20: 973  
 Median :2016-10-05 00:00:00   Median :13.00    Below 30:4587  
 Mean   :2016-10-05 00:06:58   Mean   :12.59    Below 40:4346  
 3rd Qu.:2016-10-06 00:00:00   3rd Qu.:18.00    Below 50:2085  
 Max.   :2016-10-07 00:00:00   Max.   :23.00    Below 60: 938  
                                                60 Plus :1521  
View(head(ZipWaitTest, 500))
# ggtract <- readRDS("ggtract")
StopZip_Left <- left_join(ZipWaitTest,
                          ggtract,
                          by = c("ZipCode" = "id")
                         )
str(StopZip_Left)
'data.frame':   13326084 obs. of  12 variables:
 $ ZipCode              : chr  "20001" "20001" "20001" "20001" ...
 $ Event_Time_YrMthDayHr: POSIXct, format: "2016-10-03 00:00:00" "2016-10-03 00:00:00" ...
 $ Pct80                : num  26.9 26.9 26.9 26.9 26.9 ...
 $ Event_Time_DateNew   : POSIXct, format: "2016-10-03" "2016-10-03" ...
 $ Event_Time_HrNew     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Pct80_Level          : Ord.factor w/ 7 levels "Below 10"<"Below 20"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ long                 : num  -77 -77 -77 -77 -77 ...
 $ lat                  : num  38.9 38.9 38.9 38.9 38.9 ...
 $ order                : int  6215994 6215995 6215996 6215997 6215998 6215999 6216000 6216001 6216002 6216003 ...
 $ hole                 : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ piece                : Factor w/ 42 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ group                : Factor w/ 43395 levels "00601.1","00602.1",..: 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 ...
summary(StopZip_Left)
   ZipCode          Event_Time_YrMthDayHr             Pct80         
 Length:13326084    Min.   :2016-10-03 00:00:00   Min.   :  0.1644  
 Class :character   1st Qu.:2016-10-04 08:00:00   1st Qu.: 27.2793  
 Mode  :character   Median :2016-10-05 13:00:00   Median : 33.5260  
                    Mean   :2016-10-05 12:50:44   Mean   : 39.6346  
                    3rd Qu.:2016-10-06 18:00:00   3rd Qu.: 45.2414  
                    Max.   :2016-10-07 23:00:00   Max.   :177.7933  
                                                                    
 Event_Time_DateNew            Event_Time_HrNew   Pct80_Level           long       
 Min.   :2016-10-03 00:00:00   Min.   : 0.00    Below 10: 153887   Min.   :-77.55  
 1st Qu.:2016-10-04 00:00:00   1st Qu.: 8.00    Below 20: 775558   1st Qu.:-77.16  
 Median :2016-10-05 00:00:00   Median :13.00    Below 30:4064708   Median :-77.06  
 Mean   :2016-10-05 00:10:08   Mean   :12.68    Below 40:3803555   Mean   :-77.08  
 3rd Qu.:2016-10-06 00:00:00   3rd Qu.:18.00    Below 50:2073888   3rd Qu.:-76.99  
 Max.   :2016-10-07 00:00:00   Max.   :23.00    Below 60:1005385   Max.   :-76.64  
                                                60 Plus :1449103                   
      lat            order            hole              piece         
 Min.   :38.49   Min.   :6215994   Mode :logical    1      :12835808  
 1st Qu.:38.84   1st Qu.:6352480   FALSE:13131418   2      :  298127  
 Median :38.91   Median :6464805   TRUE :194666     3      :  169063  
 Mean   :38.91   Mean   :6564079   NA's :0          4      :    8806  
 3rd Qu.:38.97   3rd Qu.:6907659                    5      :    6426  
 Max.   :39.23   Max.   :6956170                    6      :    3332  
                                                    (Other):    4522  
     group         
 20744.1:  368712  
 22202.1:  343044  
 20166.1:  327275  
 20772.1:  301665  
 20854.1:  285854  
 20015.1:  266988  
 (Other):11432546  

Test mapping functionaltiy.

map <- get_map(location = c(lon = -77.03676, lat = 38.89784), #coordinates for the White House
               source = "google",
               # maptype = "roadmap"
               zoom = 12
              )
Source : https://maps.googleapis.com/maps/api/staticmap?center=38.89784,-77.03676&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN
ggmap(map) +
  geom_polygon(aes(x = long, 
                   y = lat, 
                   group = group,
                   fill = Pct80_Level
                  ), 
               data = filter(StopZip_Left,
                             Event_Time_YrMthDayHr == as.POSIXct("2016-10-07 20:00:00")
                             # &
                             #   Stop_Zip == "20003"
                            ),
               colour = "gray1", 
               # fill = 'black', 
               alpha = .4, 
               size = .3
              ) +
# +
  # scale_fill_gradientn(colours = c("white", "royalblue4", "red"),
  #                      #  "lightsteelblue4",
  #                      # "lightpink1",
  #                      # values=cbPalette,
  #                      # values = c(1,0.5, .3, .2, .1, 0)
  #                      na.value = "black",
  #                      breaks = c(seq(0, 180, 30))
  #                      # values = rescale()
  #                     ) 
# +
  scale_fill_brewer(palette = "Spectral", # "YlOrRd" # "Set1",
                    direction = -1,
                    limits = levels(StopZip_Left$Pct80_Level)
                   )

Shiny data for mapping (used in 3rd tab).

View(head(filter(StopZip_Left,
                 Event_Time_HrNew == 15
                ),
          500
         )
    )
Shiny_WaitData_Map <- StopZip_Left %>% 
  rename(YrMthDayHr = Event_Time_YrMthDayHr,
         YrMthDay = Event_Time_DateNew,
         Hour = Event_Time_HrNew
        )
str(Shiny_WaitData_Map)
'data.frame':   13326084 obs. of  12 variables:
 $ ZipCode    : chr  "20001" "20001" "20001" "20001" ...
 $ YrMthDayHr : POSIXct, format: "2016-10-03 00:00:00" "2016-10-03 00:00:00" ...
 $ Pct80      : num  26.9 26.9 26.9 26.9 26.9 ...
 $ YrMthDay   : POSIXct, format: "2016-10-03" "2016-10-03" ...
 $ Hour       : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Pct80_Level: Ord.factor w/ 7 levels "Below 10"<"Below 20"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ long       : num  -77 -77 -77 -77 -77 ...
 $ lat        : num  38.9 38.9 38.9 38.9 38.9 ...
 $ order      : int  6215994 6215995 6215996 6215997 6215998 6215999 6216000 6216001 6216002 6216003 ...
 $ hole       : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ piece      : Factor w/ 42 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ group      : Factor w/ 43395 levels "00601.1","00602.1",..: 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 ...
Shiny_WaitData_Map_Wed <- filter(Shiny_WaitData_Map,
                                 YrMthDay == as.POSIXct("2016-10-05")
                                )
Shiny_WaitData_Map_Thu <- filter(Shiny_WaitData_Map,
                                 YrMthDay == as.POSIXct("2016-10-06")
                                )
str(Shiny_WaitData_Map_Wed)
'data.frame':   2670985 obs. of  12 variables:
 $ ZipCode    : chr  "20001" "20001" "20001" "20001" ...
 $ YrMthDayHr : POSIXct, format: "2016-10-05 00:00:00" "2016-10-05 00:00:00" ...
 $ Pct80      : num  24.4 24.4 24.4 24.4 24.4 ...
 $ YrMthDay   : POSIXct, format: "2016-10-05" "2016-10-05" ...
 $ Hour       : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Pct80_Level: Ord.factor w/ 7 levels "Below 10"<"Below 20"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ long       : num  -77 -77 -77 -77 -77 ...
 $ lat        : num  38.9 38.9 38.9 38.9 38.9 ...
 $ order      : int  6215994 6215995 6215996 6215997 6215998 6215999 6216000 6216001 6216002 6216003 ...
 $ hole       : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ piece      : Factor w/ 42 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ group      : Factor w/ 43395 levels "00601.1","00602.1",..: 8990 8990 8990 8990 8990 8990 8990 8990 8990 8990 ...
summary(Shiny_WaitData_Map_Wed)
   ZipCode            YrMthDayHr                      Pct80         
 Length:2670985     Min.   :2016-10-05 00:00:00   Min.   :  0.7088  
 Class :character   1st Qu.:2016-10-05 07:00:00   1st Qu.: 26.8931  
 Mode  :character   Median :2016-10-05 13:00:00   Median : 33.2470  
                    Mean   :2016-10-05 12:38:31   Mean   : 38.7452  
                    3rd Qu.:2016-10-05 18:00:00   3rd Qu.: 44.2554  
                    Max.   :2016-10-05 23:00:00   Max.   :177.7933  
                                                                    
    YrMthDay               Hour         Pct80_Level          long       
 Min.   :2016-10-05   Min.   : 0.00   Below 10: 26124   Min.   :-77.55  
 1st Qu.:2016-10-05   1st Qu.: 7.00   Below 20:160309   1st Qu.:-77.16  
 Median :2016-10-05   Median :13.00   Below 30:868284   Median :-77.06  
 Mean   :2016-10-05   Mean   :12.64   Below 40:732820   Mean   :-77.07  
 3rd Qu.:2016-10-05   3rd Qu.:18.00   Below 50:438313   3rd Qu.:-76.98  
 Max.   :2016-10-05   Max.   :23.00   Below 60:185217   Max.   :-76.64  
                                      60 Plus :259918                   
      lat            order            hole             piece        
 Min.   :38.49   Min.   :6215994   Mode :logical   1      :2573195  
 1st Qu.:38.84   1st Qu.:6352542   FALSE:2632503   2      :  59324  
 Median :38.91   Median :6464799   TRUE :38482     3      :  33855  
 Mean   :38.91   Mean   :6564467   NA's :0         4      :   1731  
 3rd Qu.:38.97   3rd Qu.:6907853                   5      :   1296  
 Max.   :39.23   Max.   :6956170                   6      :    672  
                                                   (Other):    912  
     group        
 20744.1:  75108  
 22202.1:  70368  
 20166.1:  65455  
 20772.1:  62985  
 20854.1:  54738  
 20015.1:  53866  
 (Other):2288465  
saveRDS(Shiny_WaitData_Map,
        "Shiny_WaitData_Map.rds"
       )
saveRDS(Shiny_WaitData_Map_Wed,
        "Shiny_WaitData_Map_Wed.rds"
       )
saveRDS(Shiny_WaitData_Map_Thu,
        "Shiny_WaitData_Map_Thu.rds"
       )

Clustering

Data prep.

rm(tract, ggtract, StopZip_Left, ZipWaitTest, Shiny_WaitData_Base, Shiny_WaitData_Map, Shiny_WaitData_Map_Wed, Shiny_WaitData_Map_Thu)
dim(NewTravTime)
[1] 2809529     128
dim(WaitTime_RteCnts)
[1] 2810109      65
str(select(NewTravTime,
           -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )
'data.frame':   2809529 obs. of  56 variables:
 $ RowNum_OG                      : int  1 3 4 5 6 7 9 10 11 12 ...
 $ UniqueLatLng                   : chr  "38.767807__-77.155136" "38.769363__-77.157082" "38.769341__-77.155136" "38.766953__-77.155113" ...
 $ group                          : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID                   : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ BusDay_EventNum                : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Bus_ID                         : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                          : chr  "S80" "S80" "S80" "S80" ...
 $ RteChange2                     : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt                       : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ DirChange2                     : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 1 2 2 2 ...
 $ Route_Direction                : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence                  : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Start_ID                       : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc                     : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StopID_Clean                   : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator               : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_Desc                      : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ countryCode                    : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State                     : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County                    : Factor w/ 11 levels "Anne Arundel",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_City                      : Factor w/ 56 levels "Accokeek","Alexandria",..: 2 2 2 2 49 49 49 49 49 49 ...
 $ Stop_Zip                       : Factor w/ 153 levels "20001","20002",..: 150 150 150 150 123 123 123 123 123 123 ...
 $ Event_Type                     : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description              : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time_Yr                  : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth                 : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date                : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day                 : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr                  : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_HrGroup             : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Min                 : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time                     : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time                 : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time                     : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Dwell_Time2                    : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time                     : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Latitude                       : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude                      : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading                        : int  199 97 276 15 119 100 274 104 241 274 ...
 $ Odometer_Distance              : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Odometer_Distance_Lag1         : int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Odometer_Distance_Mi           : num  8.25 8.55 8.79 9.49 9.67 ...
 $ TravelDistance_Ft              : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi              : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs         : num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec                 : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr                  : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph                   : num  NA 6.05 23.57 100.83 3.44 ...
 $ TravelDistance_Mi_New          : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_New_Label    : Factor w/ 5 levels "TD_Mi_SS_Mean",..: 4 4 4 4 4 4 4 3 3 4 ...
 $ TravelDistance_Mi_NewHvrs      : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_NewHvrs_Label: Factor w/ 5 levels "TD_Mi_SS_Mean",..: 4 4 4 4 4 5 4 3 3 4 ...
 $ SpeedAvg_Mph_NewHvrs           : num  NA 6.05 23.57 100.83 3.44 ...
 $ TT_Sec_New                     : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TT_Sec_New_Label               : Factor w/ 4 levels "TravelTime_Sec",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ TT_Hr_New                      : num  NA 0.05 0.01028 0.00694 0.05278 ...
str(select(NewTravTime,
           matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )
'data.frame':   2809529 obs. of  72 variables:
 $ TD_Mi_q2          : num  0.0521 0.0521 0.0521 0.0521 0.0521 ...
 $ TD_Mi_q98         : num  0.959 0.959 0.959 0.959 0.959 ...
 $ TD_Mi_SS_q5       : num  NA 0.0252 0.2422 0.7324 0.0794 ...
 $ TD_Mi_SS_q95      : num  NA 0.626 0.242 1.008 0.176 ...
 $ TD_Mi_SSHG_q5     : num  NA 0.0996 0.2422 0.7002 0.1816 ...
 $ TD_Mi_SSHG_q95    : num  NA 0.627 0.242 0.7 0.182 ...
 $ TD_Mi_Mean        : num  0.308 0.308 0.308 0.308 0.308 ...
 $ TD_Mi_Mean_F      : num  0.232 0.232 0.232 0.232 0.232 ...
 $ TD_Mi_SS_Mean     : num  NaN 0.437 0.242 0.908 0.128 ...
 $ TD_Mi_SS_Mean_F   : num  NaN 0.457 0.242 0.977 NaN ...
 $ TD_Mi_SSHG_Mean   : num  NaN 0.442 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Mean_F : num  NaN 0.491 0.242 0.7 0.182 ...
 $ TD_Mi_Med         : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_Med_F       : num  0.198 0.198 0.198 0.198 0.198 ...
 $ TD_Mi_SS_Med      : num  NA 0.512 0.242 0.962 0.128 ...
 $ TD_Mi_SS_Med_F    : num  NA 0.512 0.242 1.008 NA ...
 $ TD_Mi_SSHG_Med    : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Med_F  : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_Cnt         : int  2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 2486795 ...
 $ TD_Mi_Cnt_F       : int  2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 2387406 ...
 $ TD_Mi_SS_Cnt      : int  0 14 1 4 2 87 22 118 91 11 ...
 $ TD_Mi_SS_Cnt_F    : int  0 12 1 3 0 77 18 106 81 9 ...
 $ TD_Mi_SSHG_Cnt    : int  0 7 1 1 1 23 6 29 28 3 ...
 $ TD_Mi_SSHG_Cnt_F  : int  0 5 1 1 1 19 4 25 24 1 ...
 $ TT_Sec_q2         : num  10 10 10 10 10 10 10 10 10 10 ...
 $ TT_Sec_q98        : num  349 349 349 349 349 349 349 349 349 349 ...
 $ TT_Sec_SS_q5      : num  NA 11.9 37 30.5 172.9 ...
 $ TT_Sec_SS_q95     : num  NA 346.3 37 75.8 189.1 ...
 $ TT_Sec_SSHG_q5    : num  NA 59.6 37 25 190 11.6 236 51.5 55 8.8 ...
 $ TT_Sec_SSHG_q95   : num  NA 276 37 25 190 ...
 $ TT_Sec_Mean       : num  105 105 105 105 105 ...
 $ TT_Sec_Mean_F     : num  56.6 56.6 56.6 56.6 56.6 ...
 $ TT_Sec_SS_Mean    : num  NaN 215.8 37 58.2 181 ...
 $ TT_Sec_SS_Mean_F  : num  NaN 218.9 37 65.5 NaN ...
 $ TT_Sec_SSHG_Mean  : num  NaN 202 37 25 190 ...
 $ TT_Sec_SSHG_Mean_F: num  NaN 226 37 25 190 ...
 $ TT_Sec_Med        : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_Med_F      : num  39 39 39 39 39 39 39 39 39 39 ...
 $ TT_Sec_SS_Med     : num  NA 223.5 37 65.5 181 ...
 $ TT_Sec_SS_Med_F   : num  NA 223.5 37 65.5 NA ...
 $ TT_Sec_SSHG_Med   : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_SSHG_Med_F : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_Cnt        : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Sec_Cnt_F      : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TT_Sec_SS_Cnt     : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Sec_SS_Cnt_F   : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TT_Sec_SSHG_Cnt   : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Sec_SSHG_Cnt_F : int  0 5 1 1 1 31 4 32 32 1 ...
 $ TT_Hr_q2          : num  0.00278 0.00278 0.00278 0.00278 0.00278 ...
 $ TT_Hr_q98         : num  0.0969 0.0969 0.0969 0.0969 0.0969 ...
 $ TT_Hr_SS_q5       : num  NA 0.00331 0.01028 0.00849 0.04803 ...
 $ TT_Hr_SS_q95      : num  NA 0.0962 0.0103 0.0211 0.0525 ...
 $ TT_Hr_SSHG_q5     : num  NA 0.01656 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_q95    : num  NA 0.07653 0.01028 0.00694 0.05278 ...
 $ TT_Hr_Mean        : num  0.0291 0.0291 0.0291 0.0291 0.0291 ...
 $ TT_Hr_Mean_F      : num  0.0157 0.0157 0.0157 0.0157 0.0157 ...
 $ TT_Hr_SS_Mean     : num  NaN 0.0599 0.0103 0.0162 0.0503 ...
 $ TT_Hr_SS_Mean_F   : num  NaN 0.0608 0.0103 0.0182 NaN ...
 $ TT_Hr_SSHG_Mean   : num  NaN 0.05615 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Mean_F : num  NaN 0.06278 0.01028 0.00694 0.05278 ...
 $ TT_Hr_Med         : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_Med_F       : num  0.0108 0.0108 0.0108 0.0108 0.0108 ...
 $ TT_Hr_SS_Med      : num  NA 0.0621 0.0103 0.0182 0.0503 ...
 $ TT_Hr_SS_Med_F    : num  NA 0.0621 0.0103 0.0182 NA ...
 $ TT_Hr_SSHG_Med    : num  NA 0.06083 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Med_F  : num  NA 0.06083 0.01028 0.00694 0.05278 ...
 $ TT_Hr_Cnt         : int  2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 2802888 ...
 $ TT_Hr_Cnt_F       : int  2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 2705189 ...
 $ TT_Hr_SS_Cnt      : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Hr_SS_Cnt_F    : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TT_Hr_SSHG_Cnt    : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Hr_SSHG_Cnt_F  : int  0 5 1 1 1 31 4 32 28 1 ...
str(WaitTime_RteCnts)
'data.frame':   2810109 obs. of  65 variables:
 $ RowNum_OG                      : int  771269 510393 842137 416282 403679 478483 842251 403790 842364 403906 ...
 $ UniqueLatLng                   : chr  "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" "38.867313__-77.053574" ...
 $ group                          : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID                   : chr  "6000273--2" "6000273--2" "6000273--2" "6000273--2" ...
 $ BusDay_EventNum                : int  2 70 55 55 55 94 164 158 272 266 ...
 $ Bus_ID                         : int  2915 2719 2950 2634 2625 2674 2950 2625 2950 2625 ...
 $ Route                          : chr  "10A" "10A" "10A" "10A" ...
 $ RteChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ RouteAlt                       : Factor w/ 14 levels "1","10","11",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ DirChange2                     : Factor w/ 2 levels "Change","Same": 2 2 2 2 2 2 2 2 2 2 ...
 $ Route_Direction                : Factor w/ 12 levels "","ANTICLKW",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Stop_Sequence                  : int  55 55 55 55 55 55 55 55 55 55 ...
 $ Start_ID                       : chr  "6000273" "6000273" "6000273" "6000273" ...
 $ Start_Desc                     : chr  "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" "ARMY-NAVY DR + S HAYES ST" ...
 $ StopID_Clean                   : chr  "2" "2" "2" "2" ...
 $ StopID_Indicator               : Factor w/ 2 levels "ID_Bad","ID_OK": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_Desc                      : chr  "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" "PENTAGON INBOUND STOP" ...
 $ countryCode                    : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Stop_State                     : Factor w/ 3 levels "DC","MD","VA": 3 3 3 3 3 3 3 3 3 3 ...
 $ Stop_County                    : Factor w/ 11 levels "Anne Arundel",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_City                      : Factor w/ 56 levels "Accokeek","Alexandria",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ Stop_Zip                       : Factor w/ 153 levels "20001","20002",..: 132 132 132 132 132 132 132 132 132 132 ...
 $ Event_Type                     : int  3 4 3 3 4 3 3 4 3 4 ...
 $ Event_Description              : Factor w/ 3 levels "Serviced Stop                                     ",..: 1 3 1 1 3 1 1 3 1 3 ...
 $ Event_Time_Yr                  : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth                 : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date                : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day                 : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr                  : int  0 1 5 5 6 6 7 8 9 10 ...
 $ Event_Time_HrGroup             : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 1 1 2 2 3 3 3 3 4 4 ...
 $ Event_Time_Min                 : int  1 3 5 38 21 40 12 36 21 27 ...
 $ Event_Time                     : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Departure_Time                 : POSIXct, format: "2016-10-03 00:01:53" "2016-10-03 01:03:51" ...
 $ Dwell_Time                     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Dwell_Time2                    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Delta_Time                     : int  -210 -89 -35 149 914 253 217 1267 400 900 ...
 $ Latitude                       : num  38.9 38.9 38.9 38.9 38.9 ...
 $ Longitude                      : num  -77.1 -77.1 -77.1 -77.1 -77.1 ...
 $ Heading                        : int  23 23 23 23 23 23 23 23 23 23 ...
 $ Odometer_Distance              : int  1131407 909311 87585 80914 88439 69784 211146 212739 336615 337781 ...
 $ Odometer_Distance_Lag1         : int  1131407 908412 87585 80914 85325 69784 211146 211995 336615 337065 ...
 $ Odometer_Distance_Mi           : num  214.3 172.2 16.6 15.3 16.7 ...
 $ TravelDistance_Ft              : int  NA 899 NA NA 3114 NA NA 744 NA 716 ...
 $ TravelDistance_Mi              : num  NA 0.17 NA NA 0.59 ...
 $ TravelDistance_Mi_Hvrs         : num  0.322 0.322 0.322 0.322 0.319 ...
 $ TravelTime_Sec                 : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TravelTime_Hr                  : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ SpeedAvg_Mph                   : num  NA 3.21 NA NA 11.6 ...
 $ TravelDistance_Mi_New          : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_New_Label    : Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ TravelDistance_Mi_NewHvrs      : num  0.322 0.17 0.322 0.322 0.59 ...
 $ TravelDistance_Mi_NewHvrs_Label: Factor w/ 5 levels "TD_Mi_SS_Mean",..: 5 4 5 5 4 5 5 4 5 4 ...
 $ SpeedAvg_Mph_NewHvrs           : num  12.32 3.21 8.91 13.62 11.6 ...
 $ TT_Sec_New                     : num  94 191 130 85 183 114 183 183 124 128 ...
 $ TT_Sec_New_Label               : Factor w/ 4 levels "TravelTime_Sec",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ TT_Hr_New                      : num  0.0261 0.0531 0.0361 0.0236 0.0508 ...
 $ RouteGroup                     : num  4 4 4 4 4 4 4 4 4 4 ...
 $ MinTime                        : POSIXct, format: "2016-10-03 00:00:19" "2016-10-03 00:00:19" ...
 $ MaxTime                        : POSIXct, format: "2016-10-03 23:57:27" "2016-10-03 23:57:27" ...
 $ SampTime                       : POSIXct, format: "2016-10-03 08:00:38" "2016-10-03 02:41:02" ...
 $ NB                             : POSIXct, format: "2016-10-03 08:36:07" "2016-10-03 05:05:41" ...
 $ WaitTime_Sec2                  : num  2128 8679 1379 296 480 ...
 $ WaitTime_Min2                  : num  35.47 144.65 22.98 4.94 7.99 ...
 $ RouteStop_ID                   : Factor w/ 20897 levels "10A__2","10A__3",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ RouteStop_CntNum               : int  175 175 175 175 175 175 175 175 175 175 ...
RouteStats <- filter(WaitTime_RteCnts,
                     WaitTime_Min2 <= 180
                    ) %>% 
  mutate(SpeedAvg_Mph_TDMNH_TTSN = TravelDistance_Mi_NewHvrs / (TT_Sec_New / 60 / 60)
        ) %>% 
  group_by(Route) %>% 
  summarise(BusDayEventNum_Mean = mean(BusDay_EventNum, na.rm = TRUE),
            BusDayEventNum_Pct10 = quantile(BusDay_EventNum, probs = 0.10, na.rm = TRUE),
            BusDayEventNum_Pct25 = quantile(BusDay_EventNum, probs = 0.25, na.rm = TRUE),
            BusDayEventNum_Pct50 = quantile(BusDay_EventNum, probs = 0.50, na.rm = TRUE),
            BusDayEventNum_Pct75 = quantile(BusDay_EventNum, probs = 0.75, na.rm = TRUE),
            BusDayEventNum_Pct90 = quantile(BusDay_EventNum, probs = 0.90, na.rm = TRUE),
            StopSequence_Mean = mean(Stop_Sequence, na.rm = TRUE),
            StopSequence_Pct10 = quantile(Stop_Sequence, probs = 0.10, na.rm = TRUE),
            StopSequence_Pct25 = quantile(Stop_Sequence, probs = 0.25, na.rm = TRUE),
            StopSequence_Pct50 = quantile(Stop_Sequence, probs = 0.50, na.rm = TRUE),
            StopSequence_Pct75 = quantile(Stop_Sequence, probs = 0.75, na.rm = TRUE),
            StopSequence_Pct90 = quantile(Stop_Sequence, probs = 0.90, na.rm = TRUE),
            EventTimeHr_Mean = mean(Event_Time_Hr, na.rm = TRUE),
            EventTimeHr_Pct10 = quantile(Event_Time_Hr, probs = 0.10, na.rm = TRUE),
            EventTimeHr_Pct25 = quantile(Event_Time_Hr, probs = 0.25, na.rm = TRUE),
            EventTimeHr_Pct50 = quantile(Event_Time_Hr, probs = 0.50, na.rm = TRUE),
            EventTimeHr_Pct75 = quantile(Event_Time_Hr, probs = 0.75, na.rm = TRUE),
            EventTimeHr_Pct90 = quantile(Event_Time_Hr, probs = 0.90, na.rm = TRUE),
            DwellTime2_Mean = mean(Dwell_Time2, na.rm = TRUE),
            DwellTime2_Pct10 = quantile(Dwell_Time2, probs = 0.10, na.rm = TRUE),
            DwellTime2_Pct25 = quantile(Dwell_Time2, probs = 0.25, na.rm = TRUE),
            DwellTime2_Pct50 = quantile(Dwell_Time2, probs = 0.50, na.rm = TRUE),
            DwellTime2_Pct75 = quantile(Dwell_Time2, probs = 0.75, na.rm = TRUE),
            DwellTime2_Pct90 = quantile(Dwell_Time2, probs = 0.90, na.rm = TRUE),
            TravDistMi_Mean = mean(TravelDistance_Mi_NewHvrs, na.rm = TRUE),
            TravDistMi_Pct10 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.10, na.rm = TRUE
                                       ),
            TravDistMi_Pct25 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.25, na.rm = TRUE
                                       ),
            TravDistMi_Pct50 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.50, na.rm = TRUE
                                       ),
            TravDistMi_Pct75 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.75, na.rm = TRUE
                                       ),
            TravDistMi_Pct90 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.90, na.rm = TRUE
                                       ),
            TravTimSec_Mean = mean(TT_Sec_New, na.rm = TRUE),
            TravTimSec_Pct10 = quantile(TT_Sec_New, probs = 0.10, na.rm = TRUE),
            TravTimSec_Pct25 = quantile(TT_Sec_New, probs = 0.25, na.rm = TRUE),
            TravTimSec_Pct50 = quantile(TT_Sec_New, probs = 0.50, na.rm = TRUE),
            TravTimSec_Pct75 = quantile(TT_Sec_New, probs = 0.75, na.rm = TRUE),
            TravTimSec_Pct90 = quantile(TT_Sec_New, probs = 0.90, na.rm = TRUE),
            WaitTimMin_Mean = mean(WaitTime_Min2, na.rm = TRUE),
            WaitTimMin_Pct10 = quantile(WaitTime_Min2, probs = 0.10, na.rm = TRUE),
            WaitTimMin_Pct25 = quantile(WaitTime_Min2, probs = 0.25, na.rm = TRUE),
            WaitTimMin_Pct50 = quantile(WaitTime_Min2, probs = 0.50, na.rm = TRUE),
            WaitTimMin_Pct75 = quantile(WaitTime_Min2, probs = 0.75, na.rm = TRUE),
            WaitTimMin_Pct90 = quantile(WaitTime_Min2, probs = 0.90, na.rm = TRUE)
           ) %>% 
  as.data.frame()
str(RouteStats)
'data.frame':   268 obs. of  43 variables:
 $ Route               : chr  "10A" "10B" "10E" "11Y" ...
 $ BusDayEventNum_Mean : num  272 297 161 157 229 ...
 $ BusDayEventNum_Pct10: num  59 75 13.2 13 29 ...
 $ BusDayEventNum_Pct25: num  134 154 34 36 119 78 141 109 38 139 ...
 $ BusDayEventNum_Pct50: num  257 280 131 87 222 104 236 264 94 239 ...
 $ BusDayEventNum_Pct75: num  387 415 265 228 328 ...
 $ BusDayEventNum_Pct90: num  484 536 368 341 454 ...
 $ StopSequence_Mean   : num  28.1 35.1 23.4 25.8 22.8 ...
 $ StopSequence_Pct10  : num  6 8 4 5 5 9 7 5 5 4 ...
 $ StopSequence_Pct25  : num  15 18 12 12 12 20 16 10 10 8 ...
 $ StopSequence_Pct50  : num  28 35 24 24 23 37 31 19 21 14 ...
 $ StopSequence_Pct75  : num  42 52 35 40 34 52 46 28 32 21 ...
 $ StopSequence_Pct90  : num  50 62 42 49 40 61 55 35 43.7 28 ...
 $ EventTimeHr_Mean    : num  13.1 13.2 13.7 14.7 13.4 ...
 $ EventTimeHr_Pct10   : num  5 6 6 7 6 6 7 6 0 6 ...
 $ EventTimeHr_Pct25   : num  8 9 7 8 8 7 9 7 0 9 ...
 $ EventTimeHr_Pct50   : num  13 13 17 17 16 8 13 16 1 14 ...
 $ EventTimeHr_Pct75   : num  18 18 18 18 18 8 18 21 23 18 ...
 $ EventTimeHr_Pct90   : num  21 21 18 19 19 9 20 22 23 21 ...
 $ DwellTime2_Mean     : num  3.82 5.48 3.58 4.31 4.16 ...
 $ DwellTime2_Pct10    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct25    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct50    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct75    : num  2 3 0 6 0 0 4 3 1 4 ...
 $ DwellTime2_Pct90    : num  8 10 7 14 3 3 10 8 5 10 ...
 $ TravDistMi_Mean     : num  0.238 0.227 0.318 0.483 0.291 ...
 $ TravDistMi_Pct10    : num  0.1061 0.1009 0.106 0.1071 0.0966 ...
 $ TravDistMi_Pct25    : num  0.143 0.138 0.151 0.163 0.13 ...
 $ TravDistMi_Pct50    : num  0.195 0.187 0.203 0.25 0.21 ...
 $ TravDistMi_Pct75    : num  0.27 0.255 0.262 0.45 0.288 ...
 $ TravDistMi_Pct90    : num  0.379 0.362 0.405 0.837 0.41 ...
 $ TravTimSec_Mean     : num  83.7 71.9 183.9 260.1 101.1 ...
 $ TravTimSec_Pct10    : num  15 14 18 17 12 11 16 19 16 24 ...
 $ TravTimSec_Pct25    : num  26 22 28 29 16 ...
 $ TravTimSec_Pct50    : num  42 37 43 62 23 ...
 $ TravTimSec_Pct75    : num  69 66.2 77 118 46 ...
 $ TravTimSec_Pct90    : num  110 112 124 188 116 ...
 $ WaitTimMin_Mean     : num  26.6 26.1 43.4 45.9 40.6 ...
 $ WaitTimMin_Pct10    : num  3.47 3.63 2.73 3.89 4.18 ...
 $ WaitTimMin_Pct25    : num  8.74 9.03 6.42 9.6 10.78 ...
 $ WaitTimMin_Pct50    : num  17.8 18.4 14.1 22.1 22 ...
 $ WaitTimMin_Pct75    : num  28.5 28.9 73.3 68.7 45.3 ...
 $ WaitTimMin_Pct90    : num  53.1 52.2 144.4 136.9 123.3 ...
rownames(RouteStats) <- RouteStats$Route
str(RouteStats)
'data.frame':   268 obs. of  43 variables:
 $ Route               : chr  "10A" "10B" "10E" "11Y" ...
 $ BusDayEventNum_Mean : num  272 297 161 157 229 ...
 $ BusDayEventNum_Pct10: num  59 75 13.2 13 29 ...
 $ BusDayEventNum_Pct25: num  134 154 34 36 119 78 141 109 38 139 ...
 $ BusDayEventNum_Pct50: num  257 280 131 87 222 104 236 264 94 239 ...
 $ BusDayEventNum_Pct75: num  387 415 265 228 328 ...
 $ BusDayEventNum_Pct90: num  484 536 368 341 454 ...
 $ StopSequence_Mean   : num  28.1 35.1 23.4 25.8 22.8 ...
 $ StopSequence_Pct10  : num  6 8 4 5 5 9 7 5 5 4 ...
 $ StopSequence_Pct25  : num  15 18 12 12 12 20 16 10 10 8 ...
 $ StopSequence_Pct50  : num  28 35 24 24 23 37 31 19 21 14 ...
 $ StopSequence_Pct75  : num  42 52 35 40 34 52 46 28 32 21 ...
 $ StopSequence_Pct90  : num  50 62 42 49 40 61 55 35 43.7 28 ...
 $ EventTimeHr_Mean    : num  13.1 13.2 13.7 14.7 13.4 ...
 $ EventTimeHr_Pct10   : num  5 6 6 7 6 6 7 6 0 6 ...
 $ EventTimeHr_Pct25   : num  8 9 7 8 8 7 9 7 0 9 ...
 $ EventTimeHr_Pct50   : num  13 13 17 17 16 8 13 16 1 14 ...
 $ EventTimeHr_Pct75   : num  18 18 18 18 18 8 18 21 23 18 ...
 $ EventTimeHr_Pct90   : num  21 21 18 19 19 9 20 22 23 21 ...
 $ DwellTime2_Mean     : num  3.82 5.48 3.58 4.31 4.16 ...
 $ DwellTime2_Pct10    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct25    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct50    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct75    : num  2 3 0 6 0 0 4 3 1 4 ...
 $ DwellTime2_Pct90    : num  8 10 7 14 3 3 10 8 5 10 ...
 $ TravDistMi_Mean     : num  0.238 0.227 0.318 0.483 0.291 ...
 $ TravDistMi_Pct10    : num  0.1061 0.1009 0.106 0.1071 0.0966 ...
 $ TravDistMi_Pct25    : num  0.143 0.138 0.151 0.163 0.13 ...
 $ TravDistMi_Pct50    : num  0.195 0.187 0.203 0.25 0.21 ...
 $ TravDistMi_Pct75    : num  0.27 0.255 0.262 0.45 0.288 ...
 $ TravDistMi_Pct90    : num  0.379 0.362 0.405 0.837 0.41 ...
 $ TravTimSec_Mean     : num  83.7 71.9 183.9 260.1 101.1 ...
 $ TravTimSec_Pct10    : num  15 14 18 17 12 11 16 19 16 24 ...
 $ TravTimSec_Pct25    : num  26 22 28 29 16 ...
 $ TravTimSec_Pct50    : num  42 37 43 62 23 ...
 $ TravTimSec_Pct75    : num  69 66.2 77 118 46 ...
 $ TravTimSec_Pct90    : num  110 112 124 188 116 ...
 $ WaitTimMin_Mean     : num  26.6 26.1 43.4 45.9 40.6 ...
 $ WaitTimMin_Pct10    : num  3.47 3.63 2.73 3.89 4.18 ...
 $ WaitTimMin_Pct25    : num  8.74 9.03 6.42 9.6 10.78 ...
 $ WaitTimMin_Pct50    : num  17.8 18.4 14.1 22.1 22 ...
 $ WaitTimMin_Pct75    : num  28.5 28.9 73.3 68.7 45.3 ...
 $ WaitTimMin_Pct90    : num  53.1 52.2 144.4 136.9 123.3 ...
View(RouteStats)
RouteStats_Scaled <- select(RouteStats,
                            -Route
                           ) %>% 
  scale()
str(RouteStats_Scaled)
 num [1:268, 1:42] 0.267 0.517 -0.848 -0.893 -0.161 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:268] "10A" "10B" "10E" "11Y" ...
  ..$ : chr [1:42] "BusDayEventNum_Mean" "BusDayEventNum_Pct10" "BusDayEventNum_Pct25" "BusDayEventNum_Pct50" ...
 - attr(*, "scaled:center")= Named num [1:42] 245.2 47.5 104.8 216.2 359 ...
  ..- attr(*, "names")= chr [1:42] "BusDayEventNum_Mean" "BusDayEventNum_Pct10" "BusDayEventNum_Pct25" "BusDayEventNum_Pct50" ...
 - attr(*, "scaled:scale")= Named num [1:42] 99.4 40.1 63.8 106.6 150.5 ...
  ..- attr(*, "names")= chr [1:42] "BusDayEventNum_Mean" "BusDayEventNum_Pct10" "BusDayEventNum_Pct25" "BusDayEventNum_Pct50" ...
class(RouteStats_Scaled)
[1] "matrix"
View(RouteStats_Scaled)
message("RouteStats")
RouteStats
summary(RouteStats)
    Route           BusDayEventNum_Mean BusDayEventNum_Pct10 BusDayEventNum_Pct25
 Length:268         Min.   : 11.84      Min.   :  2.00       Min.   :  7.00      
 Class :character   1st Qu.:170.61      1st Qu.: 20.38       1st Qu.: 51.75      
 Mode  :character   Median :251.39      Median : 45.00       Median :106.12      
                    Mean   :245.25      Mean   : 47.47       Mean   :104.84      
                    3rd Qu.:315.43      3rd Qu.: 63.25       3rd Qu.:145.00      
                    Max.   :524.23      Max.   :410.20       Max.   :444.50      
 BusDayEventNum_Pct50 BusDayEventNum_Pct75 BusDayEventNum_Pct90 StopSequence_Mean
 Min.   : 12.0        Min.   : 17.0        Min.   : 19.0        Min.   : 1.948   
 1st Qu.:133.9        1st Qu.:252.5        1st Qu.:395.2        1st Qu.:13.827   
 Median :221.5        Median :375.9        Median :503.0        Median :20.877   
 Mean   :216.2        Mean   :359.0        Mean   :489.3        Mean   :21.494   
 3rd Qu.:290.5        3rd Qu.:460.2        3rd Qu.:613.8        3rd Qu.:28.037   
 Max.   :653.0        Max.   :761.2        Max.   :934.0        Max.   :49.067   
 StopSequence_Pct10 StopSequence_Pct25 StopSequence_Pct50 StopSequence_Pct75
 Min.   : 1.000     Min.   : 1.00      Min.   : 2.00      Min.   : 2.00     
 1st Qu.: 4.000     1st Qu.: 7.00      1st Qu.:14.00      1st Qu.:20.00     
 Median : 5.000     Median :11.00      Median :21.00      Median :31.00     
 Mean   : 5.374     Mean   :11.41      Mean   :21.34      Mean   :31.49     
 3rd Qu.: 7.000     3rd Qu.:14.25      3rd Qu.:28.00      3rd Qu.:41.00     
 Max.   :21.000     Max.   :32.00      Max.   :49.00      Max.   :73.00     
 StopSequence_Pct90 EventTimeHr_Mean EventTimeHr_Pct10 EventTimeHr_Pct25
 Min.   : 2.00      Min.   : 5.403   Min.   : 0.00     Min.   : 0.00    
 1st Qu.:23.60      1st Qu.:12.506   1st Qu.: 6.00     1st Qu.: 8.00    
 Median :37.00      Median :13.111   Median : 6.00     Median : 8.00    
 Mean   :37.81      Mean   :13.153   Mean   : 6.54     Mean   : 8.63    
 3rd Qu.:49.00      3rd Qu.:13.785   3rd Qu.: 7.00     3rd Qu.: 9.00    
 Max.   :90.00      Max.   :21.311   Max.   :20.00     Max.   :21.00    
 EventTimeHr_Pct50 EventTimeHr_Pct75 EventTimeHr_Pct90 DwellTime2_Mean 
 Min.   : 1.00     Min.   : 6.00     Min.   : 6.00     Min.   : 1.108  
 1st Qu.:13.00     1st Qu.:17.00     1st Qu.:18.00     1st Qu.: 3.815  
 Median :14.00     Median :18.00     Median :19.00     Median : 5.698  
 Mean   :13.93     Mean   :17.31     Mean   :19.08     Mean   : 7.757  
 3rd Qu.:16.00     3rd Qu.:18.00     3rd Qu.:21.00     3rd Qu.: 8.024  
 Max.   :22.00     Max.   :23.00     Max.   :23.00     Max.   :89.000  
 DwellTime2_Pct10   DwellTime2_Pct25 DwellTime2_Pct50  DwellTime2_Pct75 
 Min.   : 0.00000   Min.   : 0.000   Min.   : 0.0000   Min.   :  0.000  
 1st Qu.: 0.00000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.:  1.000  
 Median : 0.00000   Median : 0.000   Median : 0.0000   Median :  3.000  
 Mean   : 0.06642   Mean   : 0.166   Mean   : 0.7575   Mean   :  4.824  
 3rd Qu.: 0.00000   3rd Qu.: 0.000   3rd Qu.: 0.0000   3rd Qu.:  6.000  
 Max.   :17.80000   Max.   :44.500   Max.   :89.0000   Max.   :133.500  
 DwellTime2_Pct90 TravDistMi_Mean  TravDistMi_Pct10  TravDistMi_Pct25 
 Min.   :  0.00   Min.   :0.1508   Min.   :0.03871   Min.   :0.08197  
 1st Qu.:  7.00   1st Qu.:0.2281   1st Qu.:0.09640   1st Qu.:0.13414  
 Median : 11.00   Median :0.2652   Median :0.10114   Median :0.14630  
 Mean   : 16.02   Mean   :0.3816   Mean   :0.12799   Mean   :0.17532  
 3rd Qu.: 16.00   3rd Qu.:0.3800   3rd Qu.:0.11123   3rd Qu.:0.15909  
 Max.   :372.00   Max.   :3.4100   Max.   :3.40238   Max.   :3.40525  
 TravDistMi_Pct50 TravDistMi_Pct75 TravDistMi_Pct90  TravTimSec_Mean  
 Min.   :0.1367   Min.   :0.1680   Min.   : 0.2118   Min.   :  46.20  
 1st Qu.:0.1898   1st Qu.:0.2579   1st Qu.: 0.3378   1st Qu.:  72.68  
 Median :0.2068   Median :0.2821   Median : 0.3951   Median :  92.65  
 Mean   :0.2576   Mean   :0.3780   Mean   : 0.6888   Mean   : 168.49  
 3rd Qu.:0.2268   3rd Qu.:0.3281   3rd Qu.: 0.5138   3rd Qu.: 145.35  
 Max.   :4.3944   Max.   :4.4469   Max.   :17.2302   Max.   :2244.50  
 TravTimSec_Pct10   TravTimSec_Pct25   TravTimSec_Pct50   TravTimSec_Pct75  
 Min.   :   2.257   Min.   :   3.642   Min.   :   5.224   Min.   :   9.167  
 1st Qu.:  15.000   1st Qu.:  22.000   1st Qu.:  34.000   1st Qu.:  54.279  
 Median :  18.000   Median :  25.964   Median :  39.000   Median :  69.000  
 Mean   :  28.370   Mean   :  38.433   Mean   :  59.459   Mean   : 107.611  
 3rd Qu.:  22.050   3rd Qu.:  33.000   3rd Qu.:  50.250   3rd Qu.:  89.000  
 Max.   :1368.600   Max.   :1378.500   Max.   :1395.000   Max.   :1411.500  
 TravTimSec_Pct90 WaitTimMin_Mean WaitTimMin_Pct10  WaitTimMin_Pct25
 Min.   :  69.0   Min.   : 4.97   Min.   : 0.8875   Min.   : 1.885  
 1st Qu.: 100.9   1st Qu.:23.27   1st Qu.: 2.4651   1st Qu.: 6.260  
 Median : 122.4   Median :30.13   Median : 3.2146   Median : 8.147  
 Mean   : 220.6   Mean   :31.97   Mean   : 3.4880   Mean   : 8.832  
 3rd Qu.: 160.4   3rd Qu.:40.30   3rd Qu.: 4.0958   3rd Qu.:10.334  
 Max.   :5829.6   Max.   :90.77   Max.   :18.6955   Max.   :45.485  
 WaitTimMin_Pct50  WaitTimMin_Pct75  WaitTimMin_Pct90
 Min.   :  3.932   Min.   :  7.761   Min.   : 10.98  
 1st Qu.: 13.419   1st Qu.: 25.719   1st Qu.: 49.28  
 Median : 16.995   Median : 35.557   Median : 68.02  
 Mean   : 19.554   Mean   : 42.097   Mean   : 82.82  
 3rd Qu.: 21.938   3rd Qu.: 51.823   3rd Qu.:123.31  
 Max.   :100.381   Max.   :137.830   Max.   :157.51  
message("RouteStats_Scaled")
RouteStats_Scaled
summary(RouteStats_Scaled)
 BusDayEventNum_Mean BusDayEventNum_Pct10 BusDayEventNum_Pct25 BusDayEventNum_Pct50
 Min.   :-2.34900    Min.   :-1.13383     Min.   :-1.53337     Min.   :-1.9165     
 1st Qu.:-0.75118    1st Qu.:-0.67566     1st Qu.:-0.83201     1st Qu.:-0.7729     
 Median : 0.06179    Median :-0.06164     Median : 0.02019     Median : 0.0493     
 Mean   : 0.00000    Mean   : 0.00000     Mean   : 0.00000     Mean   : 0.0000     
 3rd Qu.: 0.70626    3rd Qu.: 0.39342     3rd Qu.: 0.62947     3rd Qu.: 0.6967     
 Max.   : 2.80761    Max.   : 9.04453     Max.   : 5.32346     Max.   : 4.0982     
 BusDayEventNum_Pct75 BusDayEventNum_Pct90 StopSequence_Mean  StopSequence_Pct10
 Min.   :-2.2718      Min.   :-2.62837     Min.   :-1.86495   Min.   :-1.6772   
 1st Qu.:-0.7075      1st Qu.:-0.52580     1st Qu.:-0.73155   1st Qu.:-0.5269   
 Median : 0.1120      Median : 0.07633     Median :-0.05882   Median :-0.1435   
 Mean   : 0.0000      Mean   : 0.00000     Mean   : 0.00000   Mean   : 0.0000   
 3rd Qu.: 0.6725      3rd Qu.: 0.69522     3rd Qu.: 0.62429   3rd Qu.: 0.6233   
 Max.   : 2.6719      Max.   : 2.48485     Max.   : 2.63089   Max.   : 5.9912   
 StopSequence_Pct25 StopSequence_Pct50 StopSequence_Pct75 StopSequence_Pct90
 Min.   :-1.89738   Min.   :-1.85293   Min.   :-1.89086   Min.   :-1.89466  
 1st Qu.:-0.80393   1st Qu.:-0.70321   1st Qu.:-0.73675   1st Qu.:-0.75174  
 Median :-0.07497   Median :-0.03253   Median :-0.03146   Median :-0.04271  
 Mean   : 0.00000   Mean   : 0.00000   Mean   : 0.00000   Mean   : 0.00000  
 3rd Qu.: 0.51731   3rd Qu.: 0.63814   3rd Qu.: 0.60971   3rd Qu.: 0.59225  
 Max.   : 3.75209   Max.   : 2.65016   Max.   : 2.66146   Max.   : 2.76168  
 EventTimeHr_Mean  EventTimeHr_Pct10 EventTimeHr_Pct25 EventTimeHr_Pct50 
 Min.   :-5.1596   Min.   :-3.1516   Min.   :-3.4927   Min.   :-4.85426  
 1st Qu.:-0.4307   1st Qu.:-0.2604   1st Qu.:-0.2548   1st Qu.:-0.34753  
 Median :-0.0284   Median :-0.2604   Median :-0.2548   Median : 0.02803  
 Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.00000  
 3rd Qu.: 0.4208   3rd Qu.: 0.2215   3rd Qu.: 0.1499   3rd Qu.: 0.77915  
 Max.   : 5.4312   Max.   : 6.4860   Max.   : 5.0067   Max.   : 3.03251  
 EventTimeHr_Pct75 EventTimeHr_Pct90  DwellTime2_Mean   DwellTime2_Pct10  
 Min.   :-5.6353   Min.   :-5.49104   Min.   :-0.7076   Min.   :-0.06109  
 1st Qu.:-0.1543   1st Qu.:-0.45204   1st Qu.:-0.4195   1st Qu.:-0.06109  
 Median : 0.3440   Median :-0.03212   Median :-0.2191   Median :-0.06109  
 Mean   : 0.0000   Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.00000  
 3rd Qu.: 0.3440   3rd Qu.: 0.80771   3rd Qu.: 0.0284   3rd Qu.:-0.06109  
 Max.   : 2.8353   Max.   : 1.64755   Max.   : 8.6454   Max.   :16.30962  
 DwellTime2_Pct25   DwellTime2_Pct50  DwellTime2_Pct75  DwellTime2_Pct90   
 Min.   :-0.06109   Min.   :-0.1351   Min.   :-0.5087   Min.   :-0.566526  
 1st Qu.:-0.06109   1st Qu.:-0.1351   1st Qu.:-0.4032   1st Qu.:-0.318965  
 Median :-0.06109   Median :-0.1351   Median :-0.1923   Median :-0.177502  
 Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.000000  
 3rd Qu.:-0.06109   3rd Qu.:-0.1351   3rd Qu.: 0.1241   3rd Qu.:-0.000673  
 Max.   :16.30962   Max.   :15.7425   Max.   :13.5702   Max.   :12.589555  
 TravDistMi_Mean     TravDistMi_Pct10   TravDistMi_Pct25   TravDistMi_Pct50  
 Min.   :-0.600624   Min.   :-0.38582   Min.   :-0.40457   Min.   :-0.35144  
 1st Qu.:-0.399492   1st Qu.:-0.13650   1st Qu.:-0.17848   1st Qu.:-0.19720  
 Median :-0.302934   Median :-0.11603   Median :-0.12575   Median :-0.14764  
 Mean   : 0.000000   Mean   : 0.00000   Mean   : 0.00000   Mean   : 0.00000  
 3rd Qu.:-0.004291   3rd Qu.:-0.07243   3rd Qu.:-0.07032   3rd Qu.:-0.08973  
 Max.   : 7.880641   Max.   :14.15064   Max.   :13.99809   Max.   :12.01760  
 TravDistMi_Pct75  TravDistMi_Pct90  TravTimSec_Mean    TravTimSec_Pct10  
 Min.   :-0.4924   Min.   :-0.3402   Min.   :-0.51997   Min.   :-0.27803  
 1st Qu.:-0.2817   1st Qu.:-0.2503   1st Qu.:-0.40741   1st Qu.:-0.14235  
 Median :-0.2249   Median :-0.2095   Median :-0.32246   Median :-0.11041  
 Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.00000   Mean   : 0.00000  
 3rd Qu.:-0.1169   3rd Qu.:-0.1248   3rd Qu.:-0.09836   3rd Qu.:-0.06729  
 Max.   : 9.5420   Max.   :11.7980   Max.   : 8.82760   Max.   :14.26984  
 TravTimSec_Pct25   TravTimSec_Pct50   TravTimSec_Pct75  TravTimSec_Pct90 
 Min.   :-0.35474   Min.   :-0.47868   Min.   :-0.6266   Min.   :-0.3503  
 1st Qu.:-0.16755   1st Qu.:-0.22470   1st Qu.:-0.3394   1st Qu.:-0.2766  
 Median :-0.12714   Median :-0.18057   Median :-0.2458   Median :-0.2270  
 Mean   : 0.00000   Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.0000  
 3rd Qu.:-0.05539   3rd Qu.:-0.08128   3rd Qu.:-0.1185   3rd Qu.:-0.1391  
 Max.   :13.66365   Max.   :11.78754   Max.   : 8.2991   Max.   :12.9596  
 WaitTimMin_Mean   WaitTimMin_Pct10  WaitTimMin_Pct25  WaitTimMin_Pct50 
 Min.   :-1.9759   Min.   :-1.4936   Min.   :-1.5835   Min.   :-1.3173  
 1st Qu.:-0.6368   1st Qu.:-0.5875   1st Qu.:-0.5864   1st Qu.:-0.5174  
 Median :-0.1345   Median :-0.1570   Median :-0.1561   Median :-0.2158  
 Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000  
 3rd Qu.: 0.6101   3rd Qu.: 0.3491   3rd Qu.: 0.3423   3rd Qu.: 0.2010  
 Max.   : 4.3041   Max.   : 8.7343   Max.   : 8.3550   Max.   : 6.8156  
 WaitTimMin_Pct75  WaitTimMin_Pct90 
 Min.   :-1.4325   Min.   :-1.7329  
 1st Qu.:-0.6833   1st Qu.:-0.8090  
 Median :-0.2728   Median :-0.3571  
 Mean   : 0.0000   Mean   : 0.0000  
 3rd Qu.: 0.4058   3rd Qu.: 0.9767  
 Max.   : 3.9941   Max.   : 1.8017  

PCA

Using caret::preProcess.

str(RouteStats)
'data.frame':   268 obs. of  43 variables:
 $ Route               : chr  "10A" "10B" "10E" "11Y" ...
 $ BusDayEventNum_Mean : num  272 297 161 157 229 ...
 $ BusDayEventNum_Pct10: num  59 75 13.2 13 29 ...
 $ BusDayEventNum_Pct25: num  134 154 34 36 119 78 141 109 38 139 ...
 $ BusDayEventNum_Pct50: num  257 280 131 87 222 104 236 264 94 239 ...
 $ BusDayEventNum_Pct75: num  387 415 265 228 328 ...
 $ BusDayEventNum_Pct90: num  484 536 368 341 454 ...
 $ StopSequence_Mean   : num  28.1 35.1 23.4 25.8 22.8 ...
 $ StopSequence_Pct10  : num  6 8 4 5 5 9 7 5 5 4 ...
 $ StopSequence_Pct25  : num  15 18 12 12 12 20 16 10 10 8 ...
 $ StopSequence_Pct50  : num  28 35 24 24 23 37 31 19 21 14 ...
 $ StopSequence_Pct75  : num  42 52 35 40 34 52 46 28 32 21 ...
 $ StopSequence_Pct90  : num  50 62 42 49 40 61 55 35 43.7 28 ...
 $ EventTimeHr_Mean    : num  13.1 13.2 13.7 14.7 13.4 ...
 $ EventTimeHr_Pct10   : num  5 6 6 7 6 6 7 6 0 6 ...
 $ EventTimeHr_Pct25   : num  8 9 7 8 8 7 9 7 0 9 ...
 $ EventTimeHr_Pct50   : num  13 13 17 17 16 8 13 16 1 14 ...
 $ EventTimeHr_Pct75   : num  18 18 18 18 18 8 18 21 23 18 ...
 $ EventTimeHr_Pct90   : num  21 21 18 19 19 9 20 22 23 21 ...
 $ DwellTime2_Mean     : num  3.82 5.48 3.58 4.31 4.16 ...
 $ DwellTime2_Pct10    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct25    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct50    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct75    : num  2 3 0 6 0 0 4 3 1 4 ...
 $ DwellTime2_Pct90    : num  8 10 7 14 3 3 10 8 5 10 ...
 $ TravDistMi_Mean     : num  0.238 0.227 0.318 0.483 0.291 ...
 $ TravDistMi_Pct10    : num  0.1061 0.1009 0.106 0.1071 0.0966 ...
 $ TravDistMi_Pct25    : num  0.143 0.138 0.151 0.163 0.13 ...
 $ TravDistMi_Pct50    : num  0.195 0.187 0.203 0.25 0.21 ...
 $ TravDistMi_Pct75    : num  0.27 0.255 0.262 0.45 0.288 ...
 $ TravDistMi_Pct90    : num  0.379 0.362 0.405 0.837 0.41 ...
 $ TravTimSec_Mean     : num  83.7 71.9 183.9 260.1 101.1 ...
 $ TravTimSec_Pct10    : num  15 14 18 17 12 11 16 19 16 24 ...
 $ TravTimSec_Pct25    : num  26 22 28 29 16 ...
 $ TravTimSec_Pct50    : num  42 37 43 62 23 ...
 $ TravTimSec_Pct75    : num  69 66.2 77 118 46 ...
 $ TravTimSec_Pct90    : num  110 112 124 188 116 ...
 $ WaitTimMin_Mean     : num  26.6 26.1 43.4 45.9 40.6 ...
 $ WaitTimMin_Pct10    : num  3.47 3.63 2.73 3.89 4.18 ...
 $ WaitTimMin_Pct25    : num  8.74 9.03 6.42 9.6 10.78 ...
 $ WaitTimMin_Pct50    : num  17.8 18.4 14.1 22.1 22 ...
 $ WaitTimMin_Pct75    : num  28.5 28.9 73.3 68.7 45.3 ...
 $ WaitTimMin_Pct90    : num  53.1 52.2 144.4 136.9 123.3 ...
Trnsfrm <- preProcess(select(RouteStats,
                             -Route
                            ),
                      method = c("BoxCox", "center", "scale", "pca")
                     )
# loadings
Trnsfrm$rotation
                              PC1          PC2          PC3           PC4
BusDayEventNum_Mean  -0.213345025 -0.122185418  0.136991585  0.1252619109
BusDayEventNum_Pct10 -0.176488618 -0.194667900  0.097315730 -0.0182792904
BusDayEventNum_Pct25 -0.199354873 -0.184168483  0.108073823  0.0179929778
BusDayEventNum_Pct50 -0.208632241 -0.137523167  0.135811726  0.1056712360
BusDayEventNum_Pct75 -0.206459107 -0.083405973  0.120392166  0.1482988325
BusDayEventNum_Pct90 -0.200989040 -0.069047396  0.103012416  0.1696041409
StopSequence_Mean    -0.231285852  0.008121666  0.038823948 -0.2403378733
StopSequence_Pct10   -0.217775617 -0.037872684 -0.007072812 -0.2540612604
StopSequence_Pct25   -0.227985601 -0.013401106  0.016868358 -0.2573328090
StopSequence_Pct50   -0.230006617  0.005171816  0.035300896 -0.2465439050
StopSequence_Pct75   -0.230621017  0.014758948  0.043653560 -0.2337951749
StopSequence_Pct90   -0.230707436  0.016643786  0.046691647 -0.2257161673
EventTimeHr_Mean     -0.032031304 -0.047871359  0.288443589  0.1485364725
EventTimeHr_Pct10     0.051024893 -0.176476379  0.071280437 -0.0758530136
EventTimeHr_Pct25    -0.009060943 -0.107197974  0.158728313 -0.0094833045
EventTimeHr_Pct50    -0.002471478  0.050164623  0.242833306  0.1171547670
EventTimeHr_Pct75    -0.065321787  0.042126165  0.243382794  0.2157986959
EventTimeHr_Pct90    -0.099632173 -0.036412358  0.156103834  0.2232787181
DwellTime2_Mean       0.132655693 -0.109405095  0.014713538  0.1496540993
DwellTime2_Pct10      0.058267164 -0.235682383  0.229586453 -0.0271184955
DwellTime2_Pct25      0.058267164 -0.235682383  0.229586453 -0.0271184955
DwellTime2_Pct50      0.084002202 -0.239632539  0.226457353 -0.0328877421
DwellTime2_Pct75      0.119151891 -0.249139831  0.201158130  0.0002263902
DwellTime2_Pct90      0.139356571 -0.143397780  0.084167421  0.0375845978
TravDistMi_Mean       0.192945756  0.132726901  0.137043475 -0.1521206592
TravDistMi_Pct10      0.069338513 -0.066559654  0.144506416 -0.2512062713
TravDistMi_Pct25      0.080428546 -0.003000609  0.145881246 -0.3247382195
TravDistMi_Pct50      0.132628235  0.043793264  0.151186281 -0.3053359648
TravDistMi_Pct75      0.162560964  0.063815718  0.145835372 -0.2464396622
TravDistMi_Pct90      0.177909394  0.089813670  0.131997708 -0.1772473195
TravTimSec_Mean       0.223740866  0.070789627  0.049470649  0.0090029514
TravTimSec_Pct10      0.132529970 -0.194566994  0.121743313 -0.0105152914
TravTimSec_Pct25      0.137005776 -0.156633961  0.076598678 -0.0017460868
TravTimSec_Pct50      0.187488635 -0.098179229  0.083695104 -0.0154983152
TravTimSec_Pct75      0.210927600 -0.052391591  0.050154352 -0.0050231115
TravTimSec_Pct90      0.235224340  0.025857638  0.017980229 -0.0312229131
WaitTimMin_Mean       0.001449158  0.315188780  0.203831468  0.1001170549
WaitTimMin_Pct10     -0.067973155  0.207434597  0.264751089 -0.0078018794
WaitTimMin_Pct25     -0.075866806  0.233238250  0.246770650 -0.0004086392
WaitTimMin_Pct50     -0.064535985  0.265183859  0.227268692  0.0167652651
WaitTimMin_Pct75     -0.007317855  0.310892582  0.207257855  0.0559859161
WaitTimMin_Pct90      0.041501097  0.294005415  0.161950175  0.1018600268
                               PC5          PC6          PC7         PC8
BusDayEventNum_Mean   0.1447360002 -0.030892880  0.037631076 -0.19513890
BusDayEventNum_Pct10 -0.0344830162  0.028854592  0.092782589 -0.19820588
BusDayEventNum_Pct25  0.0401102433  0.027788951  0.053961231 -0.19299320
BusDayEventNum_Pct50  0.1380516995 -0.002971432  0.020321256 -0.20014161
BusDayEventNum_Pct75  0.1814270140 -0.052553021  0.035147663 -0.17990323
BusDayEventNum_Pct90  0.1911383336 -0.063305704  0.008705824 -0.18076129
StopSequence_Mean    -0.0396924031 -0.102692497 -0.146256871  0.08422440
StopSequence_Pct10   -0.0929027598 -0.061375447 -0.124747003  0.05670563
StopSequence_Pct25   -0.0679866332 -0.080292660 -0.146739645  0.06104067
StopSequence_Pct50   -0.0472095913 -0.097805243 -0.147943665  0.07970285
StopSequence_Pct75   -0.0333731724 -0.107034267 -0.146094529  0.08655153
StopSequence_Pct90   -0.0258327254 -0.108718507 -0.150008606  0.08957698
EventTimeHr_Mean     -0.0001960927  0.338734907 -0.247203392  0.08267542
EventTimeHr_Pct10    -0.2873387684  0.340272763 -0.059573856 -0.24728874
EventTimeHr_Pct25    -0.1979498919  0.385125511 -0.165568681 -0.18107033
EventTimeHr_Pct50    -0.0104095044  0.320620131 -0.218878388  0.13132718
EventTimeHr_Pct75     0.2257660182  0.058267009 -0.193195619  0.32913588
EventTimeHr_Pct90     0.3319955904 -0.054652238 -0.162196450  0.26875618
DwellTime2_Mean       0.2148573200 -0.297718480  0.019307064  0.04320716
DwellTime2_Pct10     -0.1901800959 -0.035999767  0.260651970  0.18194425
DwellTime2_Pct25     -0.1901800959 -0.035999767  0.260651970  0.18194425
DwellTime2_Pct50     -0.1663806272 -0.057671479  0.217442200  0.20550899
DwellTime2_Pct75     -0.0843449802 -0.158145990  0.108964941  0.17643114
DwellTime2_Pct90      0.0939269552 -0.168867182  0.003053471 -0.05325523
TravDistMi_Mean       0.0854178306  0.094788176  0.018466990 -0.07302461
TravDistMi_Pct10      0.1302204557  0.003269329 -0.013152294  0.15545884
TravDistMi_Pct25      0.2620432224  0.109505010  0.074388364  0.12687405
TravDistMi_Pct50      0.2950907484  0.103627810  0.075898080 -0.07392845
TravDistMi_Pct75      0.2764097625  0.088435794  0.056316635 -0.16237480
TravDistMi_Pct90      0.2044908077  0.041180989  0.060332334 -0.20122680
TravTimSec_Mean      -0.1010893175 -0.040574823 -0.185163587  0.06137904
TravTimSec_Pct10     -0.0874605490 -0.243852912 -0.214502713 -0.12255240
TravTimSec_Pct25     -0.1003449769 -0.232759989 -0.347050791 -0.18478075
TravTimSec_Pct50     -0.0526481077 -0.206474160 -0.312784348 -0.16175718
TravTimSec_Pct75      0.0124588165 -0.160309248 -0.239414434 -0.14358575
TravTimSec_Pct90      0.0403621036 -0.023602012 -0.110449880 -0.01825693
WaitTimMin_Mean      -0.1464474514 -0.087799482 -0.012532111  0.02552370
WaitTimMin_Pct10     -0.0876546467 -0.120312918  0.173956080 -0.16087266
WaitTimMin_Pct25     -0.0656256180 -0.153066369  0.153128952 -0.15511293
WaitTimMin_Pct50     -0.1030975330 -0.143604116  0.117420859 -0.14166774
WaitTimMin_Pct75     -0.1551072870 -0.086511515  0.013919809 -0.02839781
WaitTimMin_Pct90     -0.1578123145 -0.042606830 -0.086789171  0.11001192
                              PC9         PC10        PC11         PC12
BusDayEventNum_Mean   0.063560439 -0.136522364  0.12476533 -0.018620079
BusDayEventNum_Pct10 -0.046578685 -0.038368960 -0.07915750 -0.266596707
BusDayEventNum_Pct25 -0.043746885 -0.021917410 -0.05971562 -0.190628048
BusDayEventNum_Pct50  0.075542948 -0.036992209  0.10819130  0.011391485
BusDayEventNum_Pct75  0.124270429 -0.183381690  0.17268182  0.014473699
BusDayEventNum_Pct90  0.060650643 -0.204616487  0.18618509  0.013218613
StopSequence_Mean    -0.111493503  0.000575047  0.06881673 -0.014323220
StopSequence_Pct10   -0.058596068  0.047002538  0.01823256  0.045382965
StopSequence_Pct25   -0.093028835  0.021821001  0.05582852  0.002184131
StopSequence_Pct50   -0.106642449  0.005144184  0.07718214 -0.013756939
StopSequence_Pct75   -0.115598258 -0.005777885  0.06925266 -0.020874511
StopSequence_Pct90   -0.119479510 -0.016802512  0.06155257 -0.019198814
EventTimeHr_Mean     -0.048986791  0.110799459 -0.05003426  0.005460650
EventTimeHr_Pct10     0.089690044  0.085374536 -0.05206700 -0.070260947
EventTimeHr_Pct25    -0.099239450  0.326953533 -0.04041950  0.040561440
EventTimeHr_Pct50    -0.006356076 -0.071653806  0.36041727  0.004171259
EventTimeHr_Pct75    -0.061513679 -0.009586697 -0.20899478  0.008391035
EventTimeHr_Pct90    -0.152683993  0.104372870 -0.35176207  0.021168702
DwellTime2_Mean      -0.125429177  0.267583791  0.14665574 -0.316293828
DwellTime2_Pct10     -0.111272334 -0.167920782 -0.04300341  0.006381880
DwellTime2_Pct25     -0.111272334 -0.167920782 -0.04300341  0.006381880
DwellTime2_Pct50     -0.045954298 -0.125232479 -0.02094990 -0.033707181
DwellTime2_Pct75     -0.002269239  0.122471469  0.17075341  0.036482743
DwellTime2_Pct90     -0.086204387  0.484922149  0.43266859  0.443692003
TravDistMi_Mean      -0.207562415 -0.169674070  0.19595673  0.038385929
TravDistMi_Pct10      0.597796738  0.170198265  0.17818391 -0.337710363
TravDistMi_Pct25      0.372747702  0.052165741 -0.14319095  0.053806019
TravDistMi_Pct50     -0.005271829 -0.058049625 -0.15588110  0.180385183
TravDistMi_Pct75     -0.171052510 -0.114134972 -0.05258372  0.087113995
TravDistMi_Pct90     -0.271204733 -0.146974176  0.11255763 -0.041472347
TravTimSec_Mean      -0.115549612 -0.055564746  0.12278000 -0.222533790
TravTimSec_Pct10      0.130894490 -0.108008058 -0.14219086  0.382111632
TravTimSec_Pct25      0.134983731 -0.170829787 -0.20806368  0.175115595
TravTimSec_Pct50      0.133514766 -0.035776320 -0.09636676 -0.164344341
TravTimSec_Pct75     -0.117895100  0.007052659 -0.02982064 -0.250838215
TravTimSec_Pct90     -0.181801907 -0.047621919  0.05783185 -0.309491457
WaitTimMin_Mean       0.094442134 -0.083099491  0.07496205  0.017204833
WaitTimMin_Pct10     -0.070798571  0.222771703 -0.18565719 -0.047748212
WaitTimMin_Pct25     -0.025972695  0.239811281 -0.14762073 -0.049515922
WaitTimMin_Pct50      0.034873039  0.193053623 -0.11191906 -0.025256758
WaitTimMin_Pct75      0.131359278 -0.063394304  0.06508752  0.021683513
WaitTimMin_Pct90      0.131386245 -0.248524008  0.16848144  0.059603479
                             PC13          PC14         PC15
BusDayEventNum_Mean   0.040470291 -1.149303e-01  0.034463170
BusDayEventNum_Pct10 -0.441781158  2.129452e-01 -0.291118691
BusDayEventNum_Pct25 -0.340250005  6.027789e-02 -0.004822248
BusDayEventNum_Pct50 -0.009781383 -3.466436e-02  0.144073196
BusDayEventNum_Pct75  0.138155710 -1.770760e-01  0.031013553
BusDayEventNum_Pct90  0.189601158 -1.938320e-01 -0.038586609
StopSequence_Mean    -0.007573300 -7.202840e-03 -0.014508092
StopSequence_Pct10    0.088483982 -5.650425e-02  0.037354509
StopSequence_Pct25    0.035312308 -1.890517e-02  0.016530139
StopSequence_Pct50   -0.005190950 -5.745415e-03 -0.003558663
StopSequence_Pct75   -0.022798448  7.695373e-03 -0.021239453
StopSequence_Pct90   -0.024816369  5.667823e-06 -0.027900145
EventTimeHr_Mean      0.040282466  3.765009e-02 -0.045900571
EventTimeHr_Pct10     0.066356406 -1.167258e-02 -0.522611489
EventTimeHr_Pct25    -0.186882228 -3.178419e-01  0.542923805
EventTimeHr_Pct50     0.241002822  5.250542e-01  0.138077898
EventTimeHr_Pct75    -0.018997964 -5.368620e-02 -0.221040464
EventTimeHr_Pct90    -0.035713327 -9.826908e-02 -0.125377056
DwellTime2_Mean      -0.235899950  2.863786e-01  0.273875821
DwellTime2_Pct10      0.087944815 -5.445812e-02  0.058038995
DwellTime2_Pct25      0.087944815 -5.445812e-02  0.058038995
DwellTime2_Pct50      0.050052872 -4.531117e-02  0.069127058
DwellTime2_Pct75     -0.016952000 -5.557556e-02 -0.021665697
DwellTime2_Pct90     -0.091501759 -1.336627e-01 -0.301798442
TravDistMi_Mean      -0.050107499  9.274181e-02 -0.135833695
TravDistMi_Pct10      0.083521773  5.483876e-02 -0.042710504
TravDistMi_Pct25     -0.164623373 -1.586947e-01  0.042172188
TravDistMi_Pct50     -0.031687831 -3.129579e-03  0.073235366
TravDistMi_Pct75      0.001499060  6.140928e-02  0.047583804
TravDistMi_Pct90     -0.009641650  1.847780e-02 -0.008312002
TravTimSec_Mean      -0.138605565 -1.406821e-01 -0.064564103
TravTimSec_Pct10     -0.127988973  2.054790e-01  0.020994337
TravTimSec_Pct25     -0.057622012  1.971883e-01  0.080570290
TravTimSec_Pct50      0.201277835 -2.674619e-02  0.069189497
TravTimSec_Pct75      0.255945465 -2.948196e-01 -0.084769061
TravTimSec_Pct90     -0.012985751 -1.913920e-01 -0.030107135
WaitTimMin_Mean      -0.203857777 -1.238392e-01 -0.007981809
WaitTimMin_Pct10      0.225821083  1.707965e-01 -0.002717426
WaitTimMin_Pct25      0.145525830  1.616852e-01 -0.036840744
WaitTimMin_Pct50      0.113967444 -5.250087e-02  0.003148641
WaitTimMin_Pct75     -0.182696605 -1.212821e-01  0.021066388
WaitTimMin_Pct90     -0.310088620 -8.971295e-02 -0.019553303
RouteStats_Pca <- predict(Trnsfrm, RouteStats) %>% 
  select(-Route)
View(RouteStats_Pca)
str(RouteStats_Pca)
'data.frame':   268 obs. of  15 variables:
 $ PC1 : num  -2.143 -3.5 0.996 2.215 -1.33 ...
 $ PC2 : num  -0.34 -0.708 2.136 2.78 2.115 ...
 $ PC3 : num  -0.2398 -0.0136 -0.1131 1.8714 0.3826 ...
 $ PC4 : num  -0.441 -0.741 -0.231 -1.692 0.286 ...
 $ PC5 : num  0.455 0.137 -1.133 -0.39 -0.422 ...
 $ PC6 : num  -0.542 -0.651 0.384 0.335 0.856 ...
 $ PC7 : num  -0.248 -0.457 -1.26 -1.574 0.945 ...
 $ PC8 : num  0.435 0.373 1.788 0.854 0.745 ...
 $ PC9 : num  -0.334 -0.845 0.458 -0.891 -0.619 ...
 $ PC10: num  0.207 0.69 -1.068 -0.403 -0.095 ...
 $ PC11: num  -0.72 -0.321 0.904 0.47 0.458 ...
 $ PC12: num  -0.347 -0.6553 0.3871 -0.0855 0.0615 ...
 $ PC13: num  0.1573 -0.0503 0.0605 0.4991 -0.2338 ...
 $ PC14: num  -0.0734 -0.1431 0.1695 0.0626 0.5568 ...
 $ PC15: num  -0.1296 -0.1389 -0.1039 -0.3495 -0.0226 ...
head(RouteStats_Pca)

PCA

Using stats::prcomp.

str(RouteStats)
'data.frame':   268 obs. of  43 variables:
 $ Route               : chr  "10A" "10B" "10E" "11Y" ...
 $ BusDayEventNum_Mean : num  272 297 161 157 229 ...
 $ BusDayEventNum_Pct10: num  59 75 13.2 13 29 ...
 $ BusDayEventNum_Pct25: num  134 154 34 36 119 78 141 109 38 139 ...
 $ BusDayEventNum_Pct50: num  257 280 131 87 222 104 236 264 94 239 ...
 $ BusDayEventNum_Pct75: num  387 415 265 228 328 ...
 $ BusDayEventNum_Pct90: num  484 536 368 341 454 ...
 $ StopSequence_Mean   : num  28.1 35.1 23.4 25.8 22.8 ...
 $ StopSequence_Pct10  : num  6 8 4 5 5 9 7 5 5 4 ...
 $ StopSequence_Pct25  : num  15 18 12 12 12 20 16 10 10 8 ...
 $ StopSequence_Pct50  : num  28 35 24 24 23 37 31 19 21 14 ...
 $ StopSequence_Pct75  : num  42 52 35 40 34 52 46 28 32 21 ...
 $ StopSequence_Pct90  : num  50 62 42 49 40 61 55 35 43.7 28 ...
 $ EventTimeHr_Mean    : num  13.1 13.2 13.7 14.7 13.4 ...
 $ EventTimeHr_Pct10   : num  5 6 6 7 6 6 7 6 0 6 ...
 $ EventTimeHr_Pct25   : num  8 9 7 8 8 7 9 7 0 9 ...
 $ EventTimeHr_Pct50   : num  13 13 17 17 16 8 13 16 1 14 ...
 $ EventTimeHr_Pct75   : num  18 18 18 18 18 8 18 21 23 18 ...
 $ EventTimeHr_Pct90   : num  21 21 18 19 19 9 20 22 23 21 ...
 $ DwellTime2_Mean     : num  3.82 5.48 3.58 4.31 4.16 ...
 $ DwellTime2_Pct10    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct25    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct50    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DwellTime2_Pct75    : num  2 3 0 6 0 0 4 3 1 4 ...
 $ DwellTime2_Pct90    : num  8 10 7 14 3 3 10 8 5 10 ...
 $ TravDistMi_Mean     : num  0.238 0.227 0.318 0.483 0.291 ...
 $ TravDistMi_Pct10    : num  0.1061 0.1009 0.106 0.1071 0.0966 ...
 $ TravDistMi_Pct25    : num  0.143 0.138 0.151 0.163 0.13 ...
 $ TravDistMi_Pct50    : num  0.195 0.187 0.203 0.25 0.21 ...
 $ TravDistMi_Pct75    : num  0.27 0.255 0.262 0.45 0.288 ...
 $ TravDistMi_Pct90    : num  0.379 0.362 0.405 0.837 0.41 ...
 $ TravTimSec_Mean     : num  83.7 71.9 183.9 260.1 101.1 ...
 $ TravTimSec_Pct10    : num  15 14 18 17 12 11 16 19 16 24 ...
 $ TravTimSec_Pct25    : num  26 22 28 29 16 ...
 $ TravTimSec_Pct50    : num  42 37 43 62 23 ...
 $ TravTimSec_Pct75    : num  69 66.2 77 118 46 ...
 $ TravTimSec_Pct90    : num  110 112 124 188 116 ...
 $ WaitTimMin_Mean     : num  26.6 26.1 43.4 45.9 40.6 ...
 $ WaitTimMin_Pct10    : num  3.47 3.63 2.73 3.89 4.18 ...
 $ WaitTimMin_Pct25    : num  8.74 9.03 6.42 9.6 10.78 ...
 $ WaitTimMin_Pct50    : num  17.8 18.4 14.1 22.1 22 ...
 $ WaitTimMin_Pct75    : num  28.5 28.9 73.3 68.7 45.3 ...
 $ WaitTimMin_Pct90    : num  53.1 52.2 144.4 136.9 123.3 ...
PcaRes <- prcomp(select(RouteStats,
                        -Route
                       ),
                 center = TRUE,
                 scale. = TRUE
                )
str(PcaRes)
List of 5
 $ sdev    : num [1:42] 3.7 2.92 2.31 1.73 1.53 ...
 $ rotation: num [1:42, 1:42] -0.0934 0.0337 -0.0481 -0.0841 -0.1032 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:42] "BusDayEventNum_Mean" "BusDayEventNum_Pct10" "BusDayEventNum_Pct25" "BusDayEventNum_Pct50" ...
  .. ..$ : chr [1:42] "PC1" "PC2" "PC3" "PC4" ...
 $ center  : Named num [1:42] 245.2 47.5 104.8 216.2 359 ...
  ..- attr(*, "names")= chr [1:42] "BusDayEventNum_Mean" "BusDayEventNum_Pct10" "BusDayEventNum_Pct25" "BusDayEventNum_Pct50" ...
 $ scale   : Named num [1:42] 99.4 40.1 63.8 106.6 150.5 ...
  ..- attr(*, "names")= chr [1:42] "BusDayEventNum_Mean" "BusDayEventNum_Pct10" "BusDayEventNum_Pct25" "BusDayEventNum_Pct50" ...
 $ x       : num [1:268, 1:42] -1.5428 -2.1749 -0.4635 0.0989 -1.0434 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:268] "10A" "10B" "10E" "11Y" ...
  .. ..$ : chr [1:42] "PC1" "PC2" "PC3" "PC4" ...
 - attr(*, "class")= chr "prcomp"
head(unclass(PcaRes$rotation))
                             PC1        PC2         PC3          PC4         PC5
BusDayEventNum_Mean  -0.09338810 -0.2730670 -0.01759169 -0.178805587  0.10602591
BusDayEventNum_Pct10  0.03370581 -0.2655559  0.06259492  0.009587185 -0.15407896
BusDayEventNum_Pct25 -0.04814982 -0.2865078  0.05176915 -0.072336986 -0.05437076
BusDayEventNum_Pct50 -0.08414405 -0.2745364 -0.01058914 -0.160976864  0.07875618
BusDayEventNum_Pct75 -0.10320703 -0.2415034 -0.03584771 -0.193510042  0.14584281
BusDayEventNum_Pct90 -0.10653834 -0.2223407 -0.04070840 -0.214169524  0.18258238
                               PC6       PC7         PC8          PC9         PC10
BusDayEventNum_Mean  -0.0939145201 0.2275389 -0.03509260  0.085349993 -0.096147637
BusDayEventNum_Pct10 -0.0217740073 0.2446381  0.13095679  0.043360507 -0.005950166
BusDayEventNum_Pct25  0.0004701741 0.2390906  0.03248930 -0.009471207 -0.017292283
BusDayEventNum_Pct50 -0.0554379332 0.2234916 -0.08994723  0.033847958 -0.111576386
BusDayEventNum_Pct75 -0.1377039889 0.2150016 -0.05893128  0.121163990 -0.122631709
BusDayEventNum_Pct90 -0.1089639919 0.2000163 -0.02482000  0.110488185 -0.115543425
                             PC11        PC12          PC13        PC14
BusDayEventNum_Mean   0.062801021 -0.03603242  0.0008623936 -0.08280278
BusDayEventNum_Pct10 -0.046015784  0.29977626 -0.2332066150  0.52178884
BusDayEventNum_Pct25 -0.001958062  0.28744503  0.1556826973  0.35680391
BusDayEventNum_Pct50  0.062125441 -0.04358467  0.1745461088  0.01903697
BusDayEventNum_Pct75  0.102023389 -0.13985501 -0.0236560860 -0.21451196
BusDayEventNum_Pct90  0.053719704 -0.13385524 -0.1264852516 -0.29283825
                             PC15        PC16         PC17         PC18
BusDayEventNum_Mean   0.005052848 -0.01508907  0.009745823  0.010680226
BusDayEventNum_Pct10  0.060660196  0.13458006 -0.016134378  0.210562820
BusDayEventNum_Pct25 -0.143177884  0.06475744  0.096873687 -0.087000597
BusDayEventNum_Pct50 -0.048141311  0.13311402 -0.102884292 -0.348212611
BusDayEventNum_Pct75 -0.046739822 -0.04324681 -0.044670328  0.008981314
BusDayEventNum_Pct90  0.165416482 -0.20053696  0.127273115  0.335945988
                              PC19        PC20        PC21         PC22        PC23
BusDayEventNum_Mean   0.0001311624  0.01481802 -0.02010332 -0.008736911  0.01507617
BusDayEventNum_Pct10  0.1890747724 -0.14361466  0.32676688  0.298994027  0.25601598
BusDayEventNum_Pct25 -0.1119779304 -0.02693647 -0.37119644 -0.419722865 -0.36146370
BusDayEventNum_Pct50 -0.0798606326  0.14967773 -0.21733581  0.167905085  0.13496970
BusDayEventNum_Pct75 -0.0091188994 -0.05319933  0.04264386  0.077945878  0.28314788
BusDayEventNum_Pct90  0.0371772577 -0.05868440  0.17356562 -0.138934213 -0.22433912
                             PC24        PC25        PC26        PC27        PC28
BusDayEventNum_Mean   0.035524341  0.01120519  0.01145701 -0.01792224 -0.05433755
BusDayEventNum_Pct10 -0.001511485  0.06567621 -0.04280989  0.04234202 -0.05983503
BusDayEventNum_Pct25 -0.268740617 -0.03687138  0.01106133  0.03624843  0.11537293
BusDayEventNum_Pct50  0.467832249 -0.03563624 -0.05659897 -0.22924263 -0.27832184
BusDayEventNum_Pct75 -0.060158497 -0.16655286  0.29788147  0.18922670  0.52573534
BusDayEventNum_Pct90 -0.173530963  0.20837876 -0.20790238  0.01226040 -0.30881153
                             PC29        PC30        PC31        PC32        PC33
BusDayEventNum_Mean  -0.027244823 -0.03406674  0.01164006  0.01939078  0.01947718
BusDayEventNum_Pct10  0.005149411  0.01270162  0.02074997  0.03403886  0.04113859
BusDayEventNum_Pct25  0.080478872 -0.01417978 -0.04301737 -0.05300308 -0.05209785
BusDayEventNum_Pct50 -0.243481810  0.08604567  0.01413502  0.01985978  0.06027302
BusDayEventNum_Pct75  0.156224102 -0.18879008  0.05609357 -0.03700094 -0.20358027
BusDayEventNum_Pct90  0.034396647  0.16745680 -0.09754177  0.04105972  0.15253964
                             PC34         PC35          PC36        PC37
BusDayEventNum_Mean   0.002353764  0.185633777  0.0014790395  0.20337749
BusDayEventNum_Pct10  0.012954051 -0.012131682  0.0059264541 -0.01199984
BusDayEventNum_Pct25 -0.018879547 -0.031598554  0.0001730291 -0.02891357
BusDayEventNum_Pct50  0.016046771 -0.081880339 -0.0125889072 -0.02732315
BusDayEventNum_Pct75 -0.011931747  0.003619082  0.0037123974 -0.08662220
BusDayEventNum_Pct90 -0.018446717 -0.076652688  0.0006734246 -0.04129042
                            PC38          PC39         PC40         PC41
BusDayEventNum_Mean  -0.83310830 -0.0555982816 -0.017040143 -0.021911140
BusDayEventNum_Pct10  0.05102906  0.0014005972  0.001480221 -0.000404305
BusDayEventNum_Pct25  0.08830438  0.0050649048  0.005018847  0.001109075
BusDayEventNum_Pct50  0.25817484  0.0262306950 -0.008653629  0.013151507
BusDayEventNum_Pct75  0.22914052  0.0219838671  0.004533696 -0.001389741
BusDayEventNum_Pct90  0.26868985 -0.0004870527  0.018278493  0.010426258
                              PC42
BusDayEventNum_Mean   0.000000e+00
BusDayEventNum_Pct10  1.137111e-15
BusDayEventNum_Pct25 -1.240408e-15
BusDayEventNum_Pct50 -7.054637e-16
BusDayEventNum_Pct75  1.098402e-15
BusDayEventNum_Pct90  1.144457e-16
PcaRes_Vars <- get_pca_var(PcaRes)
PcaRes_Vars
Principal Component Analysis Results for variables
 ===================================================
  Name       Description                                    
1 "$coord"   "Coordinates for the variables"                
2 "$cor"     "Correlations between variables and dimensions"
3 "$cos2"    "Cos2 for the variables"                       
4 "$contrib" "contributions of the variables"               
# Where variables lie in relation to the eigenvectors
PcaRes_Vars$coord
                             Dim.1       Dim.2        Dim.3         Dim.4
BusDayEventNum_Mean  -0.3453868585 -0.79864826 -0.040549393 -0.3101788419
BusDayEventNum_Pct10  0.1246576734 -0.77668029  0.144283261  0.0166311473
BusDayEventNum_Pct25 -0.1780774431 -0.83795908  0.119329510 -0.1254849071
BusDayEventNum_Pct50 -0.3111986196 -0.80294590 -0.024408294 -0.2792508785
BusDayEventNum_Pct75 -0.3817012137 -0.70633309 -0.082630086 -0.3356870531
BusDayEventNum_Pct90 -0.3940217542 -0.65028732 -0.093834131 -0.3715256112
StopSequence_Mean    -0.6062202976 -0.63368485 -0.000250384  0.3681810417
StopSequence_Pct10   -0.5104512059 -0.55305750  0.131422129  0.4549991039
StopSequence_Pct25   -0.5870433756 -0.61971675  0.064032900  0.4156613851
StopSequence_Pct50   -0.6037565277 -0.63004661  0.011954416  0.3789532825
StopSequence_Pct75   -0.6060170236 -0.63112642 -0.017434449  0.3547384747
StopSequence_Pct90   -0.6056307545 -0.63241456 -0.027117609  0.3373818795
EventTimeHr_Mean      0.1409841911 -0.45123485 -0.434425667 -0.5392611879
EventTimeHr_Pct10     0.3650802311 -0.18481759  0.140564337 -0.0005859469
EventTimeHr_Pct25     0.1812387365 -0.34043913 -0.153840693 -0.1319928923
EventTimeHr_Pct50     0.0998863834 -0.23186174 -0.460244684 -0.4198494889
EventTimeHr_Pct75    -0.0496537949 -0.34904266 -0.464505273 -0.6021833375
EventTimeHr_Pct90    -0.1238963525 -0.40981359 -0.226375988 -0.6201279862
DwellTime2_Mean       0.8761725891 -0.08851345  0.023548293 -0.0391522334
DwellTime2_Pct10      0.7030526682 -0.51973983  0.023351765  0.1159456061
DwellTime2_Pct25      0.7030526682 -0.51973983  0.023351765  0.1159456061
DwellTime2_Pct50      0.7598191952 -0.45628425  0.033756487  0.1016314507
DwellTime2_Pct75      0.8567433124 -0.36032024  0.060406952  0.0661690571
                             Dim.5         Dim.6        Dim.7        Dim.8
BusDayEventNum_Mean   0.1620448429 -0.1237663077  0.286822488 -0.035185407
BusDayEventNum_Pct10 -0.2354867822 -0.0286951206  0.308376700  0.131303136
BusDayEventNum_Pct25 -0.0830976196  0.0006196242  0.301383902  0.032575226
BusDayEventNum_Pct50  0.1203671065 -0.0730595045  0.281720681 -0.090185116
BusDayEventNum_Pct75  0.2228990527 -0.1814747521  0.271018677 -0.059087140
BusDayEventNum_Pct90  0.2790500120 -0.1435994235  0.252129115 -0.024885637
StopSequence_Mean     0.1502459692  0.1660744495 -0.189215347  0.006633515
StopSequence_Pct10    0.0891931972  0.2237366241 -0.097986712 -0.026684850
StopSequence_Pct25    0.1212047481  0.2075489453 -0.147387480 -0.002888018
StopSequence_Pct50    0.1409645113  0.1766307694 -0.184145228  0.002573750
StopSequence_Pct75    0.1561083910  0.1569403785 -0.196455551  0.009423167
StopSequence_Pct90    0.1621689839  0.1479344469 -0.203929297  0.012425368
EventTimeHr_Mean     -0.2976406788  0.3611790281 -0.217197870 -0.035433483
EventTimeHr_Pct10    -0.5875325743  0.4544560234  0.263701295 -0.048631537
EventTimeHr_Pct25    -0.5127704888  0.5356588751  0.145248857 -0.145064347
EventTimeHr_Pct50    -0.2372086273  0.3844329548 -0.233176335 -0.032996948
EventTimeHr_Pct75     0.1182887213 -0.0303309505 -0.428088045  0.049596566
EventTimeHr_Pct90     0.2803410616 -0.1150452357 -0.357162484  0.000334219
DwellTime2_Mean       0.3265850529  0.0121630989  0.009739963 -0.102156699
DwellTime2_Pct10     -0.2636000040 -0.2115407053 -0.047004287  0.250779277
DwellTime2_Pct25     -0.2636000040 -0.2115407053 -0.047004287  0.250779277
DwellTime2_Pct50     -0.2179272276 -0.1758810591 -0.087807917  0.232529164
DwellTime2_Pct75     -0.0008099102 -0.1147971824 -0.114338424 -0.033366524
                             Dim.9       Dim.10        Dim.11       Dim.12
BusDayEventNum_Mean   0.0813826783 -0.081069520  0.0462348712 -0.024349934
BusDayEventNum_Pct10  0.0413449850 -0.005017046 -0.0338773772  0.202582359
BusDayEventNum_Pct25 -0.0090309577 -0.014580463 -0.0014415492  0.194249180
BusDayEventNum_Pct50  0.0322746072 -0.094078694  0.0457375013 -0.029453587
BusDayEventNum_Pct75  0.1155319368 -0.103400293  0.0751108534 -0.094511011
BusDayEventNum_Pct90  0.1053523749 -0.097423611  0.0395490958 -0.090456495
StopSequence_Mean     0.0384816056  0.021593551  0.0004048936  0.015622886
StopSequence_Pct10    0.0001000936 -0.009691535  0.0124519915 -0.054353562
StopSequence_Pct25    0.0167808943  0.006077356  0.0125369547 -0.018280995
StopSequence_Pct50    0.0387279886  0.015011648  0.0033734128  0.007930651
StopSequence_Pct75    0.0420721101  0.026003417 -0.0026818750  0.020028619
StopSequence_Pct90    0.0427386307  0.028961923 -0.0015467238  0.027909000
EventTimeHr_Mean     -0.0673379520  0.031012680 -0.0165592080  0.005965818
EventTimeHr_Pct10    -0.0006979970 -0.017882118 -0.0190645441  0.048214425
EventTimeHr_Pct25    -0.1885746677  0.072593235  0.0726647407  0.155906584
EventTimeHr_Pct50     0.0974080030 -0.200682500 -0.0094097458 -0.411753703
EventTimeHr_Pct75    -0.0084453835  0.172797161 -0.0749370011  0.118497628
EventTimeHr_Pct90    -0.1158461142  0.289621254 -0.1227874388  0.112833597
DwellTime2_Mean      -0.1183567247 -0.050082868  0.0274029577  0.095865161
DwellTime2_Pct10      0.1206958380  0.076920817 -0.0376909074 -0.056673828
DwellTime2_Pct25      0.1206958380  0.076920817 -0.0376909074 -0.056673828
DwellTime2_Pct50      0.1081772424  0.019181631 -0.1150009473  0.014517843
DwellTime2_Pct75      0.0715248085  0.077271116 -0.0558586129  0.037998358
                            Dim.13       Dim.14       Dim.15        Dim.16
BusDayEventNum_Mean   0.0005167747 -0.047570404  0.002425721 -0.0066602099
BusDayEventNum_Pct10 -0.1397450859  0.299768981  0.029121141  0.0594026800
BusDayEventNum_Pct25  0.0932902007  0.204984730 -0.068735407  0.0285834721
BusDayEventNum_Pct50  0.1045937783  0.010936786 -0.023111199  0.0587555818
BusDayEventNum_Pct75 -0.0141755060 -0.123237654 -0.022438387 -0.0190888355
BusDayEventNum_Pct90 -0.0757941294 -0.168236297  0.079411491 -0.0885155840
StopSequence_Mean    -0.0240452988  0.011363881 -0.014966637 -0.0737578689
StopSequence_Pct10    0.0518286399 -0.106219129  0.111863344  0.3171542212
StopSequence_Pct25    0.0070811784 -0.034301981  0.031199729  0.0635940150
StopSequence_Pct50   -0.0169389599  0.011063201 -0.004932205 -0.0636781924
StopSequence_Pct75   -0.0312723530  0.027802520 -0.024992556 -0.1129812048
StopSequence_Pct90   -0.0372327148  0.026953963 -0.036298428 -0.1321627299
EventTimeHr_Mean     -0.0307188539 -0.039769118  0.004635036  0.0228034928
EventTimeHr_Pct10    -0.3868700922 -0.149147121  0.047434196 -0.0036960152
EventTimeHr_Pct25     0.3279595796 -0.136473389 -0.032616028 -0.0870215474
EventTimeHr_Pct50     0.0491970084  0.241784024  0.060255232  0.0002987279
EventTimeHr_Pct75    -0.0894314375 -0.022018923 -0.017960172  0.0700084976
EventTimeHr_Pct90    -0.0392403183 -0.014684949 -0.032214959  0.0278113336
DwellTime2_Mean       0.0468692445  0.056696376  0.209275868 -0.0183507211
DwellTime2_Pct10      0.0635984540 -0.049637715  0.030751717 -0.0126587792
DwellTime2_Pct25      0.0635984540 -0.049637715  0.030751717 -0.0126587792
DwellTime2_Pct50      0.0723555858 -0.050133150  0.122463044 -0.0228010059
DwellTime2_Pct75      0.0455601347  0.002206588  0.212127496 -0.0698035974
                           Dim.17       Dim.18        Dim.19       Dim.20
BusDayEventNum_Mean   0.004053993  0.003812342  0.0000452259  0.004920803
BusDayEventNum_Pct10 -0.006711456  0.075161098  0.0651945705 -0.047691882
BusDayEventNum_Pct25  0.040296780 -0.031055152 -0.0386109315 -0.008945123
BusDayEventNum_Pct50 -0.042797025 -0.124295648 -0.0275366173  0.049705322
BusDayEventNum_Pct75 -0.018581623  0.003205910 -0.0031442731 -0.017666554
BusDayEventNum_Pct90  0.052942102  0.119917036  0.0128190309 -0.019488049
StopSequence_Mean    -0.009303088 -0.009559360  0.0119120365  0.016866209
StopSequence_Pct10    0.045349275  0.045543172 -0.0459816126 -0.049385578
StopSequence_Pct25   -0.002067620  0.020114063  0.0007124121 -0.007712216
StopSequence_Pct50   -0.017457007 -0.003956252  0.0123207652  0.013197655
StopSequence_Pct75   -0.009535570 -0.020114558  0.0122675119  0.023013928
StopSequence_Pct90   -0.011506402 -0.016756976  0.0173595805  0.027994258
EventTimeHr_Mean     -0.013535163 -0.054116912  0.0262138237 -0.002274056
EventTimeHr_Pct10    -0.042868499 -0.037564564 -0.0423537688  0.049825363
EventTimeHr_Pct25     0.051981139  0.066097079 -0.0013513232 -0.020813588
EventTimeHr_Pct50    -0.030557635  0.006462641  0.0086339618  0.005666899
EventTimeHr_Pct75     0.004644992  0.017113443  0.0148970297  0.008548211
EventTimeHr_Pct90    -0.009177214  0.029866953 -0.0385430889 -0.017418715
DwellTime2_Mean      -0.019303612  0.029122928 -0.0144632230  0.089022119
DwellTime2_Pct10      0.013710612  0.023812384  0.0225802785  0.031484673
DwellTime2_Pct25      0.013710612  0.023812384  0.0225802785  0.031484673
DwellTime2_Pct50     -0.038827136 -0.028228576 -0.0305634324  0.024951143
DwellTime2_Pct75     -0.047699538 -0.050300691 -0.0435822558 -0.036105695
                            Dim.21        Dim.22       Dim.23        Dim.24
BusDayEventNum_Mean  -6.310695e-03 -0.0023823659  0.003722401  0.0078284159
BusDayEventNum_Pct10  1.025764e-01  0.0815291755  0.063211948 -0.0003330823
BusDayEventNum_Pct25 -1.165234e-01 -0.1144493069 -0.089247650 -0.0592217411
BusDayEventNum_Pct50 -6.822457e-02  0.0457840689  0.033324863  0.1030950985
BusDayEventNum_Pct75  1.338647e-02  0.0212541475  0.069910983 -0.0132569872
BusDayEventNum_Pct90  5.448453e-02 -0.0378843414 -0.055390732 -0.0382406124
StopSequence_Mean     2.141301e-03 -0.0008767539  0.001473267  0.0022539848
StopSequence_Pct10   -9.429553e-05 -0.0072540956 -0.012726560 -0.0102877892
StopSequence_Pct25    1.036487e-03  0.0101805796  0.006022349  0.0062252760
StopSequence_Pct50    6.041747e-03  0.0045960029  0.002553015  0.0077275469
StopSequence_Pct75    1.262483e-03 -0.0005165235 -0.001221804  0.0039562629
StopSequence_Pct90   -3.415561e-03 -0.0017967417 -0.002802168 -0.0007275000
EventTimeHr_Mean     -1.760218e-02  0.0082595781  0.005773987  0.0195931212
EventTimeHr_Pct10    -2.479089e-02 -0.0135454891 -0.038699425  0.0122454257
EventTimeHr_Pct25     5.125354e-02  0.0212507063  0.028577039 -0.0095561036
EventTimeHr_Pct50     7.033257e-03  0.0113720747 -0.023812393 -0.0340671019
EventTimeHr_Pct75     3.936493e-02 -0.1359559053  0.092639975 -0.0035091885
EventTimeHr_Pct90    -2.571477e-02  0.1269598276 -0.097181072  0.0078862915
DwellTime2_Mean       6.229252e-02 -0.0137054563 -0.041384412  0.0327874766
DwellTime2_Pct10      1.412795e-02  0.0101168179 -0.042555885  0.0106898753
DwellTime2_Pct25      1.412795e-02  0.0101168179 -0.042555885  0.0106898753
DwellTime2_Pct50     -1.759360e-02 -0.0065513421  0.029210609 -0.0084795311
DwellTime2_Pct75     -8.191325e-02 -0.0189190228  0.067313100 -0.0205330465
                            Dim.25        Dim.26        Dim.27        Dim.28
BusDayEventNum_Mean   2.274769e-03  0.0022549762 -3.129965e-03 -0.0090042162
BusDayEventNum_Pct10  1.333295e-02 -0.0084258722  7.394671e-03 -0.0099151989
BusDayEventNum_Pct25 -7.485269e-03  0.0021770983  6.330476e-03  0.0191183228
BusDayEventNum_Pct50 -7.234523e-03 -0.0111398481 -4.003526e-02 -0.0461204114
BusDayEventNum_Pct75 -3.381194e-02  0.0586292399  3.304682e-02  0.0871190342
BusDayEventNum_Pct90  4.230303e-02 -0.0409194913  2.141173e-03 -0.0511728240
StopSequence_Mean    -1.965571e-03  0.0005382572 -2.467380e-03  0.0010152450
StopSequence_Pct10    3.196022e-03  0.0064521814  3.436764e-03 -0.0011476906
StopSequence_Pct25   -5.595126e-05 -0.0004687659  1.461262e-03  0.0035162963
StopSequence_Pct50    1.166179e-03 -0.0022671642  8.910097e-05  0.0030037459
StopSequence_Pct75   -3.367941e-03 -0.0005201314 -5.233024e-03  0.0001038537
StopSequence_Pct90   -4.711327e-03  0.0005565750 -5.749750e-03  0.0001517165
EventTimeHr_Mean     -2.654675e-02 -0.0567252434  4.393663e-02 -0.0323942338
EventTimeHr_Pct10     4.000136e-03  0.0310441236 -1.285666e-02  0.0128737330
EventTimeHr_Pct25     1.229891e-02  0.0226937123 -1.156552e-02  0.0055517463
EventTimeHr_Pct50     1.305587e-02  0.0143256285 -1.270386e-02  0.0195568668
EventTimeHr_Pct75    -2.981156e-02 -0.0127535911 -3.550813e-02  0.0070284952
EventTimeHr_Pct90     3.705157e-02  0.0403544364  9.864400e-03  0.0157652542
DwellTime2_Mean      -2.650585e-03  0.0085754261 -9.444301e-02  0.0290588516
DwellTime2_Pct10     -6.170268e-02 -0.0079264619  7.303716e-03 -0.0019073143
DwellTime2_Pct25     -6.170268e-02 -0.0079264619  7.303716e-03 -0.0019073143
DwellTime2_Pct50     -5.970478e-03  0.0074308014  1.965785e-02  0.0156426314
DwellTime2_Pct75      9.558740e-02  0.0219012201  3.372714e-02 -0.0201269523
                            Dim.29        Dim.30        Dim.31        Dim.32
BusDayEventNum_Mean  -0.0041916024 -0.0047035001  0.0013576329  0.0020875101
BusDayEventNum_Pct10  0.0007922342  0.0017536768  0.0024201627  0.0036644461
BusDayEventNum_Pct25  0.0123816341 -0.0019577622 -0.0050173112 -0.0057060343
BusDayEventNum_Pct50 -0.0374595540  0.0118800870  0.0016486313  0.0021380006
BusDayEventNum_Pct75  0.0240349995 -0.0260657217  0.0065424480 -0.0039833281
BusDayEventNum_Pct90  0.0052919068  0.0231202960 -0.0113767398  0.0044202755
StopSequence_Mean     0.0022701521 -0.0014270289  0.0032879882  0.0066635155
StopSequence_Pct10    0.0019227195 -0.0075530544  0.0167257013  0.0351970398
StopSequence_Pct25   -0.0030686694  0.0093150099 -0.0309898265 -0.0744584980
StopSequence_Pct50    0.0065783326  0.0008642006 -0.0177539581 -0.0246282656
StopSequence_Pct75   -0.0009191151 -0.0044454775  0.0122153215  0.0203658402
StopSequence_Pct90   -0.0012335055 -0.0024593334  0.0167940718  0.0407345523
EventTimeHr_Mean      0.0765277937 -0.0545117383 -0.0024338661 -0.0020714705
EventTimeHr_Pct10    -0.0219921000  0.0111865131  0.0024188066 -0.0013087163
EventTimeHr_Pct25    -0.0187581252  0.0152738660  0.0022742693  0.0027029130
EventTimeHr_Pct50    -0.0203390185  0.0201581873  0.0002943196  0.0018295472
EventTimeHr_Pct75    -0.0320036386  0.0259483826  0.0065242035 -0.0035861908
EventTimeHr_Pct90    -0.0178294381  0.0020603224 -0.0055413484  0.0018972260
DwellTime2_Mean       0.0476316972 -0.0261889632 -0.0006741801 -0.0031350598
DwellTime2_Pct10     -0.0094901336  0.0035665255  0.0292524947 -0.0100339033
DwellTime2_Pct25     -0.0094901336  0.0035665255  0.0292524947 -0.0100339033
DwellTime2_Pct50     -0.0161521672 -0.0014006964 -0.0816016644  0.0312323996
DwellTime2_Pct75      0.0052308677  0.0023356343  0.0402489533 -0.0152891208
                            Dim.33        Dim.34        Dim.35        Dim.36
BusDayEventNum_Mean   0.0019174296  0.0001632924  1.180630e-02  7.934989e-05
BusDayEventNum_Pct10  0.0040498849  0.0008986878 -7.715742e-04  3.179520e-04
BusDayEventNum_Pct25 -0.0051287684 -0.0013097693 -2.009666e-03  9.282942e-06
BusDayEventNum_Pct50  0.0059335725  0.0011132454 -5.207584e-03 -6.753900e-04
BusDayEventNum_Pct75 -0.0200414436 -0.0008277654  2.301734e-04  1.991687e-04
BusDayEventNum_Pct90  0.0150167531 -0.0012797417 -4.875106e-03  3.612897e-05
StopSequence_Mean    -0.0012949113  0.0005160241  5.337299e-04  8.744366e-04
StopSequence_Pct10   -0.0008471353  0.0002321344  1.492560e-03 -4.315794e-03
StopSequence_Pct25    0.0042135732 -0.0026518637 -2.038217e-03  2.439069e-02
StopSequence_Pct50   -0.0007762503 -0.0014603898 -5.736917e-03 -3.912882e-02
StopSequence_Pct75   -0.0029224987  0.0015217648  5.199057e-03 -6.228567e-03
StopSequence_Pct90   -0.0001781289  0.0017918090  1.374886e-03  2.479692e-02
EventTimeHr_Mean      0.0169091353  0.0020482488  2.373909e-03  1.387977e-03
EventTimeHr_Pct10    -0.0092632055 -0.0015629600 -3.998048e-04 -4.523724e-04
EventTimeHr_Pct25    -0.0014031995  0.0003230656 -4.000983e-04 -5.500721e-04
EventTimeHr_Pct50    -0.0034199825 -0.0010049657  2.074391e-05 -5.830054e-05
EventTimeHr_Pct75    -0.0057114594 -0.0006949630  9.108066e-04 -1.252353e-03
EventTimeHr_Pct90    -0.0068554462 -0.0008813521 -5.106255e-03  7.478921e-05
DwellTime2_Mean      -0.0060020507  0.0012130849 -4.579912e-04  1.151039e-03
DwellTime2_Pct10     -0.0019643683 -0.0035892526 -1.382631e-03 -1.972075e-05
DwellTime2_Pct25     -0.0019643683 -0.0035892526 -1.382631e-03 -1.972075e-05
DwellTime2_Pct50      0.0109535045  0.0120062255 -1.776263e-04  8.877460e-04
DwellTime2_Pct75     -0.0030097804 -0.0083966376  2.866777e-03 -2.249948e-04
                            Dim.37        Dim.38        Dim.39        Dim.40
BusDayEventNum_Mean   9.566998e-03 -3.363389e-02 -1.863510e-03 -4.617860e-04
BusDayEventNum_Pct10 -5.644794e-04  2.060123e-03  4.694439e-05  4.011383e-05
BusDayEventNum_Pct25 -1.360112e-03  3.564986e-03  1.697625e-04  1.360102e-04
BusDayEventNum_Pct50 -1.285297e-03  1.042292e-02  8.791850e-04 -2.345124e-04
BusDayEventNum_Pct75 -4.074760e-03  9.250762e-03  7.368423e-04  1.228626e-04
BusDayEventNum_Pct90 -1.942326e-03  1.084743e-02 -1.632475e-05  4.953451e-04
StopSequence_Mean    -9.087007e-04 -9.412291e-04  6.541437e-04 -2.630648e-04
StopSequence_Pct10   -6.384350e-04  4.634691e-04  6.674373e-04  5.108676e-04
StopSequence_Pct25    5.391859e-05 -5.529682e-04 -3.312383e-03 -1.737062e-03
StopSequence_Pct50    5.271147e-03 -8.992085e-04  1.187709e-02  1.801878e-03
StopSequence_Pct75   -2.010497e-03  3.010653e-03 -2.612996e-02  5.039646e-04
StopSequence_Pct90   -1.747338e-03 -4.642906e-04  1.633786e-02 -8.388692e-04
EventTimeHr_Mean     -1.070934e-04  9.776305e-04 -7.624559e-05 -4.575407e-04
EventTimeHr_Pct10     8.373793e-05  6.981350e-05  1.456376e-04  1.412071e-04
EventTimeHr_Pct25     5.643783e-05 -2.865494e-04 -1.006636e-04  5.655169e-05
EventTimeHr_Pct50    -5.803978e-04 -7.935715e-04  2.962251e-05  3.213865e-04
EventTimeHr_Pct75    -8.439012e-04  8.317391e-05  2.851096e-04  1.961147e-05
EventTimeHr_Pct90     9.874299e-04 -3.435562e-04 -3.745431e-04  1.607303e-04
DwellTime2_Mean      -1.220655e-03 -8.832596e-05  7.004446e-05 -4.934088e-04
DwellTime2_Pct10      6.545549e-03  1.424356e-03  1.965091e-04  3.571272e-04
DwellTime2_Pct25      6.545549e-03  1.424356e-03  1.965091e-04  3.571272e-04
DwellTime2_Pct50     -2.610665e-03 -9.572892e-04 -1.084876e-03 -9.380582e-04
DwellTime2_Pct75     -1.422715e-03  1.470083e-05  6.958196e-04  6.322145e-04
                            Dim.41        Dim.42
BusDayEventNum_Mean  -2.744270e-04  0.000000e+00
BusDayEventNum_Pct10 -5.063735e-06  2.923130e-31
BusDayEventNum_Pct25  1.389066e-05 -3.188672e-31
BusDayEventNum_Pct50  1.647166e-04 -1.813510e-31
BusDayEventNum_Pct75 -1.740587e-05  2.823621e-31
BusDayEventNum_Pct90  1.305841e-04  2.942013e-32
StopSequence_Mean     1.111001e-02 -6.048767e-32
StopSequence_Pct10   -3.732037e-04  4.220146e-31
StopSequence_Pct25   -1.303292e-03 -1.880061e-31
StopSequence_Pct50   -2.471636e-03  1.104379e-31
StopSequence_Pct75   -3.130287e-03 -6.970740e-32
StopSequence_Pct90   -3.926392e-03 -1.002304e-31
EventTimeHr_Mean     -5.052287e-05  1.123309e-31
EventTimeHr_Pct10     1.745821e-05 -2.717004e-31
EventTimeHr_Pct25     1.158883e-05  2.292590e-31
EventTimeHr_Pct50     5.550145e-05  1.339744e-32
EventTimeHr_Pct75    -6.726525e-05 -4.833701e-32
EventTimeHr_Pct90     7.423506e-05 -8.943109e-32
DwellTime2_Mean      -1.100660e-05  4.376558e-31
DwellTime2_Pct10      4.840789e-05 -1.817734e-16
DwellTime2_Pct25      4.840789e-05  1.817734e-16
DwellTime2_Pct50      5.752855e-05  6.421522e-32
DwellTime2_Pct75     -7.283262e-05 -2.455485e-32
 [ reached getOption("max.print") -- omitted 19 rows ]
# Graph of the Factor-Variable Map
fviz_pca_var(PcaRes,
             col.var = "contrib"
            ) +
  scale_color_gradient2(low = "white",
                        mid = "blue", 
                        high = "red",
                        midpoint = 2
                       )

# Graph of the Factor-Variable Map (top 10 contributing variables)
fviz_pca_var(PcaRes,
             col.var = "contrib",
             select.var = list(contrib = 10)
            ) +
  scale_color_gradient2(low = "white",
                        mid = "blue", 
                        high = "red",
                        midpoint = 3.8
                       )

PcaRes_Rtes <- get_pca_ind(PcaRes)
PcaRes_Rtes
Principal Component Analysis Results for individuals
 ===================================================
  Name       Description                       
1 "$coord"   "Coordinates for the individuals" 
2 "$cos2"    "Cos2 for the individuals"        
3 "$contrib" "contributions of the individuals"
# Where routes lie in relation to the eigenvectors
PcaRes_Rtes$coord
            Dim.1        Dim.2         Dim.3        Dim.4        Dim.5        Dim.6
10A  -1.542836562  -1.16606749   0.733765527  0.057073840  0.590293129 -0.269474784
10B  -2.174899598  -2.53491936   0.733466454  0.688172993  0.669526180  0.494469415
10E  -0.463521733   1.65146836  -1.178721490  0.337496580 -0.648636145  0.260889810
11Y   0.098927711   1.17610835  -2.083752449  0.773359544 -0.516913593  1.115537010
15K  -1.043392379   0.30982796  -1.210203039 -0.029434585 -0.460845248 -0.130844848
15L  -1.894257885   1.62934766   4.398957144  7.212378021 -0.104278063  0.210713042
16A  -1.435101124  -1.62802533   1.448749115  0.524381702 -0.001912895  0.704811101
16B  -0.788573489  -0.27880075  -2.718996161 -1.625838089 -0.099908303 -0.885757776
16E  -1.922710359   1.64106507  -4.312513228  1.577890278  3.320449289 -5.795946883
16G   0.060304638   0.08737982   2.874418347 -2.364824129 -0.354269527 -0.194825770
16H   0.236105907   1.70906049   0.656357075 -0.864689093 -0.729125000 -0.046153278
16J  -0.285626404  -0.14184890   1.496359076 -0.288857495 -0.637278511  0.036502493
16L  -0.810616482   0.66969878  -2.103607144  0.864761642 -0.654968161  0.526228622
16X   1.828629979   3.68928326  -0.306993995  0.470061832 -0.781990660  0.179404703
16Y   2.399925187   3.62975175   0.680504819 -0.615273989 -1.166996087  0.822837198
17A  -2.006100069  -1.51481085  -5.164208278  1.664709429 -0.680549043  0.398939090
17B  -2.353817207  -2.31352049  -7.565353870  3.281847281 -1.797837271  1.541832049
17F   0.480565180   2.84099795  -1.152850541 -0.171283324 -1.638291450 -0.694490494
17G  -0.408234254   0.04980661  -2.493250807 -0.460998538 -2.121401980  2.087848459
17H  -0.814605999   1.27136745  -0.532942392  0.710896403 -0.193748099 -0.005766723
17K  -0.397241210   1.30236239  -2.277268263  0.029700607 -0.706863834 -0.138775928
17L   0.238497659   2.21804711  -1.828323379  0.511129338 -0.543385946  0.351670925
17M  -0.746352768   0.11407475  -2.114505818 -0.207910583 -2.079078219  1.903358765
            Dim.7         Dim.8        Dim.9       Dim.10        Dim.11
10A  -0.540367721  1.551212e-01 -0.328117744  0.656714911 -0.4170391975
10B  -0.529313879  4.005257e-02 -0.299677622  0.687140405 -0.3350517536
10E  -2.120380643  1.259242e-01  1.257079027 -0.888086064  0.5883003707
11Y  -2.119144999  1.798144e-01  0.484448941 -0.283582271  0.1424017421
15K  -0.638335829  2.919903e-01  0.109018868 -0.406269694  0.0730366667
15L   1.517671710  5.944777e-01  0.019794831 -0.885017840  0.3695757808
16A  -0.488284743  8.642134e-02 -0.572554465  0.806854117 -0.5809893915
16B  -0.980745758  2.327846e-02  0.024616625  0.054566496 -0.0165284480
16E  -1.316861978  7.235517e-01  0.455887132  1.562106321 -0.1353112953
16G   0.071793936 -3.005923e-02 -0.340220097  0.638041536 -0.6164829761
16H  -1.328478608  1.614517e-02  0.882968488 -0.654285415  0.4305184983
16J   0.543467272  1.992937e-03 -0.769694179  0.583212200 -0.5108630660
16L  -1.714113165  3.794170e-01  0.227432067 -0.335961855  0.1314186713
16X  -1.622654968  1.713166e-01  0.200780269  0.010452167 -0.1300623128
16Y  -2.164718095  7.978942e-03  0.558469942  0.291329602 -0.2839775307
17A  -1.180469061  5.280818e-01 -1.198278691  0.495337044 -0.5078286445
17B  -0.518191330 -2.330708e-02 -1.887760616  0.953746011 -0.3167097852
17F   0.274475417  4.866159e-01  0.260293836 -0.740756620  0.2753899630
17G  -1.055669614 -4.333289e-01  0.286616090 -0.547564952  0.7090156954
17H  -1.547953601  4.093002e-01  0.627720359 -0.546435124  0.2382929880
17K  -1.550774702  3.063733e-01  0.759985580 -0.925001096  0.5097783613
17L  -1.582955879  5.530625e-01  0.666288443 -0.615716064  0.1234530057
17M  -1.118511068 -2.352449e-01  0.035033068 -0.361935228  0.5903282113
            Dim.12        Dim.13        Dim.14       Dim.15       Dim.16
10A   0.1085083078  0.1672523070  0.3486424017 -0.449362680 -0.038659885
10B   0.3629438935 -0.0023235871  0.2499113792 -0.124767481 -0.052584070
10E  -0.3073345989 -0.2423335539  0.2307324710 -0.183325899 -0.272525859
11Y  -0.2544707284 -0.5281010223  0.0828313408  0.051742551 -0.393440792
15K  -0.2972289148  0.0437322222  0.4301664690 -0.209985371 -0.058811147
15L  -0.5707953689  0.6698126081  0.4174604490 -0.083650782 -0.708826004
16A   0.3295214811 -0.2402660029  0.4247855424 -0.154346949 -0.001074633
16B   0.1199254929 -0.4967669202  0.0236699419  0.079140626  0.644575589
16E   2.9484655351 -1.0431595377 -1.9240599248 -0.924242048  1.110643904
16G  -0.0691715294  0.3760164819  0.4637970731 -0.450519573  0.235878185
16H  -0.1721855700  0.0538656280  0.2529483405  0.124584407 -0.119143975
16J   0.1869807429  0.0242267971  0.6174463618 -0.143221684  0.135550323
16L  -0.2379391493 -0.2561391109  0.6667982400 -0.133839204 -0.334947572
16X  -0.2636414956 -0.1175255861  1.0433250676 -0.192102821 -0.104018091
16Y   0.0534781633 -0.1237712268  0.6794705460 -0.007505770  0.130256379
17A  -0.0540582525 -1.1411471510  0.7224590339  0.224723798  0.543993758
17B   1.1977892244 -0.6789405799 -0.3654368012 -0.068420682 -0.448087392
17F   0.0288690312 -0.2676161572  0.3441671009 -0.067726885  0.549610548
17G   0.8335888212  1.5453371477 -0.5653356017 -0.437291360 -0.475288073
17H  -0.7852585489 -0.3336994462 -0.0507602545 -0.049463926 -0.101064126
17K  -0.2127585145 -0.2059316181  0.1139681958 -0.039972401  0.329551984
17L  -0.5896070173 -0.2243446191  0.2604331894 -0.297960700  0.224934985
17M   0.4871726527  1.2256196756 -0.7206707324 -0.353898032 -0.757894818
           Dim.17       Dim.18       Dim.19       Dim.20        Dim.21
10A  -0.013989013 -0.049868209  0.061545482 -0.084062650 -0.0983152717
10B   0.008636602  0.042057595  0.022471412 -0.013127038 -0.0021023650
10E  -0.119011201 -0.163744553  0.380561610  0.267625834  0.1522980433
11Y  -0.196816764 -0.082404663  0.202429902  0.146022130 -0.1590100282
15K   0.257663084 -0.218068072 -0.375916152  0.087211955 -0.2141753651
15L  -0.012545901 -0.256813841  0.326222308  0.077214124  0.2502478735
16A  -0.215860672 -0.099730270  0.193992005  0.016900928  0.1131454550
16B  -0.019170397 -0.354611999 -0.065258351  0.165165811 -0.1289252193
16E  -0.484655622 -0.071137668  1.023560381  0.087613501  0.3730182467
16G  -0.228927184  0.100673133  0.263636461 -0.238272450  0.0382544747
16H   0.194810282  0.220575660 -0.061929741 -0.008861366  0.1365928158
16J  -0.156695072 -0.197701944  0.117131462 -0.089118844 -0.0971720136
16L   0.144383872 -0.062549673  0.163883900 -0.021130362  0.0834282740
16X   0.111294383 -0.164290203 -0.089418314 -0.012024437 -0.4626582575
16Y  -0.090510333  0.121393066 -0.274133484 -0.170833748 -0.6993443280
17A   0.145767335 -0.261150332  0.001298097 -0.024317832  0.2764324683
17B  -0.031574888 -0.242159763  0.248602436  0.173409802  0.5314320374
17F   0.229085883 -0.111440155  0.325356370 -0.300834524  0.2382675085
17G   0.212139340  0.091445864  0.193084048 -0.034468405  0.1789528124
17H   0.253848088  0.211973463 -0.108651201 -0.195360779  0.1446983544
17K  -0.114931613 -0.347985921  0.450107227  0.030509389  0.0902583994
17L  -0.024864542 -0.071807530  0.475218271 -0.197583546  0.0317659302
17M   0.382004602  0.526932781  0.195730610 -0.120343106  0.7699906035
            Dim.22       Dim.23       Dim.24        Dim.25       Dim.26
10A   0.1910511609  0.039952051  0.048352252 -1.206459e-01 -0.094848404
10B   0.1696005577 -0.011715907  0.071547755 -6.368711e-02  0.008063785
10E  -0.0296019210 -0.102295642  0.253815378 -6.867801e-02 -0.403296313
11Y   0.0329405654 -0.124179181 -0.002352048  7.619184e-02 -0.161695012
15K  -0.1783619880 -0.174330537 -0.098005803 -3.168179e-01  0.074213788
15L   0.2296205655  0.128039391 -0.117697778 -4.499667e-03  0.001470069
16A  -0.0680234014  0.165737266  0.086713588 -3.669788e-02 -0.109061735
16B  -0.2423812506 -0.115164790  0.057436994 -3.470089e-02 -0.231868686
16E  -0.6714655797  0.539770050 -0.554714241 -2.810791e-01 -0.097693441
16G   0.0138043301  0.034392436 -0.076679541  1.039382e-01 -0.242004327
16H  -0.2551255472  0.138782046 -0.356604383 -5.900252e-02 -0.126506567
16J  -0.1001269000  0.109004624  0.004533672  2.780563e-03 -0.217265763
16L  -0.0301108065 -0.247538363  0.094109030 -7.547833e-03 -0.212679374
16X   0.2360426383  0.129288593 -0.183080133 -1.894825e-01 -0.176325794
16Y   0.1563783462  0.306614528 -0.064463497 -2.730040e-01 -0.118265520
17A   0.0819645977 -0.089288592 -0.176905396 -9.529823e-02 -0.149363370
17B  -0.0599566134  0.028929108 -0.163394539  5.243189e-02  0.062402939
17F   0.0258402700  0.061349755 -0.141277093  4.552904e-02  0.050163417
17G   0.1811841545 -0.072525505  0.428024877  1.569601e-01 -0.167857070
17H  -0.2236006781  0.215991166 -0.087942770 -2.656066e-01  0.348434556
17K  -0.1022069446 -0.458441004  0.055546960  2.712463e-01 -0.099490102
17L  -0.2463476148 -0.143590115  0.122541269 -6.159793e-02  0.003143265
17M   0.2618337869  0.062126217 -0.117203758  2.158792e-01  0.094389097
            Dim.27       Dim.28        Dim.29        Dim.30        Dim.31
10A  -1.013436e-02  0.024539295 -0.0358041539 -3.056426e-03 -7.652263e-02
10B  -8.881169e-02  0.038354010 -0.0156378260  2.555456e-03 -5.736526e-03
10E   1.057239e-01  0.310522062 -0.0414056828  9.981591e-02 -9.967208e-02
11Y   3.977547e-01  0.051099506  0.1761480887 -1.657971e-01  2.113169e-01
15K  -2.375772e-01 -0.077862039 -0.0200442560  9.564503e-02 -8.620410e-02
15L  -4.598725e-03  0.074410940  0.0308073194 -2.003688e-02 -3.463860e-02
16A  -8.113217e-02  0.106145880  0.0144738901 -5.924624e-02  7.418179e-02
16B  -1.012332e-01  0.101719661 -0.1091143838  6.940196e-02  8.747023e-02
16E   2.919003e-01  0.016987499  0.2868972065 -2.221013e-01  9.848382e-02
16G   4.018050e-02  0.047957565  0.0112625275 -5.254871e-02  6.604319e-02
16H  -2.151748e-02 -0.016017119  0.1096180105  1.519586e-01  9.296455e-02
16J   1.973718e-02  0.015866061  0.0704226861 -1.197704e-01  8.264083e-02
16L   1.494477e-01 -0.006433867 -0.0517057099 -8.643427e-02 -8.569243e-02
16X  -1.970587e-01  0.050607542 -0.1164434843  4.770605e-03  3.142338e-01
16Y  -1.183576e-01 -0.065444610 -0.1789311048 -2.441205e-01 -3.970071e-03
17A   3.606205e-02  0.190320800  0.1285290335 -1.188451e-01  9.442775e-04
17B  -5.569296e-02  0.344136544 -0.1368148252 -1.683090e-02  2.656214e-01
17F  -1.097538e-01 -0.087012718 -0.3890297423  7.298743e-02  6.086590e-02
17G   1.652829e-01  0.132142245 -0.0920719158 -1.003628e-01  2.341681e-02
17H  -1.039334e-01  0.165672616 -0.2705183587  2.295024e-02  5.062636e-02
17K   3.653419e-01  0.211204721  0.0236354695 -1.348201e-01  2.221987e-02
17L   3.238312e-02  0.080238408 -0.1724782633 -2.430069e-01  2.727123e-02
17M   1.849442e-01  0.159513046 -0.0745783075  1.745692e-01 -6.270878e-02
            Dim.32       Dim.33        Dim.34        Dim.35        Dim.36
10A  -0.1223256205 -0.020059746  0.1194390888  0.0543429302  2.867830e-02
10B  -0.0015439141 -0.068836548  0.0799320925  0.0313239954 -4.413682e-02
10E  -0.1342565896 -0.029934617 -0.0035791000 -0.0646967657  9.698481e-03
11Y   0.1080767888 -0.001744475 -0.0339235106  0.0868923889  1.159281e-01
15K  -0.1230649365  0.044419428  0.0279388866 -0.0810218554 -3.067368e-02
15L  -0.0598993641  0.037654778  0.0924660485  0.0000845747 -3.596857e-02
16A  -0.0265748607 -0.063275076 -0.0331315430 -0.0664700874 -1.763365e-02
16B  -0.0322444449  0.041229080 -0.0026382302 -0.0197615717 -4.058804e-03
16E   0.0603459075 -0.049094642  0.0341469870  0.0098914580  2.817218e-02
16G   0.0891736174  0.023679770 -0.0539855128 -0.1553602715  1.076101e-01
16H  -0.1256860510  0.084041500 -0.0316745683 -0.0052972243 -1.756423e-02
16J  -0.0872764048  0.002034392 -0.0543255669 -0.0567901160  2.412947e-02
16L  -0.0116558957 -0.037001103 -0.0339680784 -0.0757795677 -1.185639e-02
16X   0.3287694141 -0.121772793  0.0353983372  0.0412649544  8.775987e-02
16Y  -0.0107983969  0.002788849 -0.0652782973  0.0101150727  1.308197e-02
17A  -0.0237782035 -0.003399762  0.0155663206  0.0050761569 -5.187226e-02
17B   0.3446517437  0.174104807  0.0614652837  0.1162741038  7.172430e-02
17F  -0.0005331575  0.207543459  0.1106501889 -0.0768172155 -4.107588e-02
17G   0.0032972205  0.140240210  0.0159059548 -0.0330534142 -4.040581e-02
17H   0.1181950178 -0.248731411 -0.0258424604 -0.0082373793  9.214950e-04
17K  -0.0539215245  0.082287207 -0.0168460649  0.0928107986  8.299477e-02
17L   0.2391192750 -0.162302081 -0.0113720083  0.0429648257 -4.523328e-02
17M  -0.0495782125  0.118884975 -0.0052960587  0.0055044837  6.907606e-03
            Dim.37        Dim.38        Dim.39        Dim.40        Dim.41
10A   0.0416880305 -5.623081e-03 -0.0269754746 -1.311137e-02 -1.361863e-02
10B   0.0282017940  1.210562e-02 -0.0060365120 -1.065691e-02  1.074055e-02
10E   0.0064114242  1.149354e-02 -0.0049071460 -2.104260e-03 -8.509691e-03
11Y   0.0702982041 -8.662284e-02 -0.0616978887 -1.984622e-02 -1.530568e-02
15K  -0.0633117886  1.266739e-02 -0.0154061888  1.091886e-02  9.814985e-04
15L  -0.0522812607 -1.349988e-02  0.0223477372  1.718584e-02 -9.140548e-04
16A   0.0135941857  5.562863e-06 -0.0063763248 -5.397158e-03 -1.609453e-02
16B  -0.0073879151  3.232388e-02  0.0501574816 -4.955605e-03  3.866150e-03
16E   0.0253005890 -1.283040e-01  0.1012134379 -5.157183e-03  2.818900e-02
16G  -0.0222446528 -3.745610e-02  0.0387190467 -1.928684e-02 -2.730989e-02
16H  -0.0208155349 -3.671872e-02  0.0187277006  1.214933e-02  1.027403e-02
16J  -0.0125374151 -2.574236e-02  0.0137887303 -1.240361e-02  2.514162e-03
16L  -0.0043211000 -3.282431e-03  0.0304821163  6.666663e-03  1.076047e-03
16X   0.0797448730  5.515680e-02 -0.0812024851 -8.595630e-03  5.034341e-03
16Y  -0.0082263049 -2.080322e-02  0.0317844562 -1.595780e-02 -4.773846e-03
17A  -0.0554788580 -4.194835e-02 -0.0038693634  1.460534e-02  1.054139e-02
17B  -0.0753540463  7.905749e-02 -0.0946624982  6.917386e-04  8.770804e-03
17F  -0.0267404768  7.699470e-02  0.0016387862 -1.820436e-02 -1.801550e-02
17G  -0.0025781825  2.631362e-02  0.0042653944 -1.707652e-02  6.987287e-03
17H  -0.0682425779  3.419874e-02 -0.0248683173  6.631064e-03  1.433760e-02
17K   0.0129758868  6.158344e-02  0.0167577060 -1.724061e-02  3.464283e-03
17L  -0.0245398153  2.305695e-02 -0.0073531128  4.755591e-03 -1.891973e-03
17M  -0.0057349445  9.716078e-04  0.0139556034 -6.196076e-04  3.880775e-04
            Dim.42
10A  -1.623002e-16
10B   4.678794e-16
10E  -4.156912e-16
11Y  -3.659179e-16
15K  -2.211667e-15
15L  -1.000575e-15
16A  -9.426823e-16
16B  -3.699428e-16
16E  -1.493855e-15
16G  -1.386722e-15
16H   2.746736e-16
16J  -1.378688e-15
16L  -1.351729e-15
16X  -1.361262e-15
16Y  -1.755417e-15
17A  -3.305707e-16
17B   6.249625e-16
17F  -6.862838e-16
17G   2.433032e-15
17H  -8.310768e-16
17K   1.210537e-15
17L  -5.372654e-16
17M   3.241721e-15
 [ reached getOption("max.print") -- omitted 245 rows ]
# Graph of Route Map
fviz_pca_ind(PcaRes,
             col.ind="cos2"
            ) +
  scale_color_gradient2(low = "white",
                        mid = "blue",
                        high = "red",
                        midpoint = 0.50
                       ) +
  # comment out xlim and ylim to see EXTREME outlier Routes
  xlim(-5, 5) +
  ylim(-5, 5)

# Graph of Route Map (top 10 contributing variables)
fviz_pca_ind(PcaRes,
             col.ind="cos2",
             select.var = list(cos2 = 10)
            ) +
  scale_color_gradient2(low = "white",
                        mid = "blue",
                        high = "red",
                        midpoint = 0.50
                       ) +
  # comment out xlim and ylim to see EXTREME outlier Routes
  xlim(-5, 5) +
  ylim(-5, 5)

# Inspecting what looks to be an EXTREME outlier route
View(filter(WaitTime_RteCnts,
            Route == "SH99"
           )
    )
# Biplot of Routes and Variables
fviz_pca_biplot(PcaRes,  geom = "text") +
  xlim(-5, 5) +
  ylim(-5, 5)

# 9 eigenvalues give ~ 90% of the variance
# "elbow" at ~6th Principal Component
# ~ 8 eigenvalues > 1 (PC accounts for more variance than accounted the original standardized variables)
View(get_eigenvalue(PcaRes))
fviz_screeplot(PcaRes, ncp = 15)

fviz_screeplot(PcaRes, ncp = 15, choice = "eigenvalue")

# Create a dataframe for the "top" 8 PCs
RouteStats_Pca_8Eign <- as.data.frame(PcaRes_Rtes$coord) %>% 
  select(Dim.1,
         Dim.2,
         Dim.3,
         Dim.4,
         Dim.5,
         Dim.6,
         Dim.7,
         Dim.8
        )
View(RouteStats_Pca_8Eign)

Clustering (using the Principal Components computed using caret::preProcess).

Are the data clusterable?

##### Are the data clusterable?
# gradient_col <- list(low = "steelblue", high = "white")
ClustData_Ends <- get_clust_tendency(RouteStats_Pca,
                                     n = nrow(RouteStats_Pca
                                             ) - 1,
                                     # gradient = gradient_col,
                                     seed = 123456789
                                    )
str(ClustData_Ends)
List of 2
 $ hopkins_stat: num 0.166
 $ plot        :List of 9
  ..$ data       :'data.frame': 71824 obs. of  3 variables:
  .. ..$ Var1 : Factor w/ 268 levels "r202-","r210-",..: 1 2 3 4 5 6 7 8 9 10 ...
  .. ..$ Var2 : Factor w/ 268 levels "r202-","r210-",..: 1 1 1 1 1 1 1 1 1 1 ...
  .. ..$ value: num [1:71824] 0 13.7 11.4 10.6 12.9 ...
  ..$ layers     :List of 1
  .. ..$ :Classes 'LayerInstance', 'Layer', 'ggproto' <ggproto object: Class LayerInstance, Layer>
    aes_params: list
    compute_aesthetics: function
    compute_geom_1: function
    compute_geom_2: function
    compute_position: function
    compute_statistic: function
    data: waiver
    draw_geom: function
    finish_statistics: function
    geom: <ggproto object: Class GeomTile, GeomRect, Geom>
        aesthetics: function
        default_aes: uneval
        draw_group: function
        draw_key: function
        draw_layer: function
        draw_panel: function
        extra_params: na.rm width height
        handle_na: function
        non_missing_aes: 
        optional_aes: 
        parameters: function
        required_aes: x y
        setup_data: function
        use_defaults: function
        super:  <ggproto object: Class GeomRect, Geom>
    geom_params: list
    inherit.aes: TRUE
    layer_data: function
    map_statistic: function
    mapping: uneval
    position: <ggproto object: Class PositionIdentity, Position>
        compute_layer: function
        compute_panel: function
        required_aes: 
        setup_data: function
        setup_params: function
        super:  <ggproto object: Class Position>
    print: function
    show.legend: NA
    stat: <ggproto object: Class StatIdentity, Stat>
        aesthetics: function
        compute_group: function
        compute_layer: function
        compute_panel: function
        default_aes: uneval
        extra_params: na.rm
        finish_layer: function
        non_missing_aes: 
        parameters: function
        required_aes: 
        retransform: TRUE
        setup_data: function
        setup_params: function
        super:  <ggproto object: Class Stat>
    stat_params: list
    subset: NULL
    super:  <ggproto object: Class Layer> 
  ..$ scales     :Classes 'ScalesList', 'ggproto' <ggproto object: Class ScalesList>
    add: function
    clone: function
    find: function
    get_scales: function
    has_scale: function
    input: function
    n: function
    non_position_scales: function
    scales: list
    super:  <ggproto object: Class ScalesList> 
  ..$ mapping    :List of 2
  .. ..$ x: symbol Var1
  .. ..$ y: symbol Var2
  ..$ theme      :List of 4
  .. ..$ axis.title.x: list()
  .. .. ..- attr(*, "class")= chr [1:2] "element_blank" "element"
  .. ..$ axis.title.y: list()
  .. .. ..- attr(*, "class")= chr [1:2] "element_blank" "element"
  .. ..$ axis.text   : list()
  .. .. ..- attr(*, "class")= chr [1:2] "element_blank" "element"
  .. ..$ axis.ticks  : list()
  .. .. ..- attr(*, "class")= chr [1:2] "element_blank" "element"
  .. ..- attr(*, "class")= chr [1:2] "theme" "gg"
  .. ..- attr(*, "complete")= logi FALSE
  .. ..- attr(*, "validate")= logi FALSE
  ..$ coordinates:Classes 'CoordCartesian', 'Coord', 'ggproto' <ggproto object: Class CoordCartesian, Coord>
    aspect: function
    distance: function
    expand: TRUE
    is_linear: function
    labels: function
    limits: list
    range: function
    render_axis_h: function
    render_axis_v: function
    render_bg: function
    render_fg: function
    train: function
    transform: function
    super:  <ggproto object: Class CoordCartesian, Coord> 
  ..$ facet      :Classes 'FacetNull', 'Facet', 'ggproto' <ggproto object: Class FacetNull, Facet>
    compute_layout: function
    draw_back: function
    draw_front: function
    draw_labels: function
    draw_panels: function
    finish_data: function
    init_scales: function
    map: function
    map_data: function
    params: list
    render_back: function
    render_front: function
    render_panels: function
    setup_data: function
    setup_params: function
    shrink: TRUE
    train: function
    train_positions: function
    train_scales: function
    vars: function
    super:  <ggproto object: Class FacetNull, Facet> 
  ..$ plot_env   :<environment: 0x221cfda70> 
  ..$ labels     :List of 3
  .. ..$ x   : chr "Var1"
  .. ..$ y   : chr "Var2"
  .. ..$ fill: chr "value"
  ..- attr(*, "class")= chr [1:2] "gg" "ggplot"
# Hopkins statistic
ClustData_Ends$hopkins_stat  # value of 0.1657494 implies that the data are not uniformly distributed (they are "clusterable")
[1] 0.1657494
#plot
ClustData_Ends$plot

Clustering. How many clusters are there?

kmeans, pam, and hierarchical clustring methods, using within sum of squares and silhouette measures.

# class(RouteStats_Pca)
fviz_nbclust(RouteStats_Pca, kmeans, method = "wss")  # ~8 clusters

fviz_nbclust(RouteStats_Pca, pam, method = "wss")  # ~6 clusters

fviz_nbclust(RouteStats_Pca, hcut, method = "wss")  # ~6 clusters

fviz_nbclust(RouteStats_Pca, kmeans, method = "silhouette")  # 2 clusters

fviz_nbclust(RouteStats_Pca, pam, method = "silhouette")  # 2 clusters

fviz_nbclust(RouteStats_Pca, hcut, method = "silhouette",
             hc_method = "complete")  # 2 clusters

Clustering. How many clusters are there?

kmeans method with the gap statistic, using bootstrap.

# Compute gap statistic
# kmeans version
set.seed(123456789)
# system.time(
gap_stat_km <- clusGap(RouteStats_Pca,
                       FUN = kmeans,
                       nstart = 25,
                       K.max = 10,
                       B = 500
                      )
Clustering k = 1,2,..., K.max (= 10): .. done
Bootstrapping, b = 1,2,..., B (= 500)  [one "." per sample]:
.................................................. 50 
.................................................. 100 
.................................................. 150 
.................................................. 200 
.................................................. 250 
.................................................. 300 
.................................................. 350 
.................................................. 400 
.................................................. 450 
.................................................. 500 
# )
# Print
print(gap_stat_km, method = "Tibs2001SEmax")
Clustering Gap statistic ["clusGap"] from call:
clusGap(x = RouteStats_Pca, FUNcluster = kmeans, K.max = 10,     B = 500, nstart = 25)
B=500 simulated reference sets, k = 1..10; spaceH0="scaledPCA"
 --> Number of clusters (method 'Tibs2001SEmax', SE.factor=1): 1
          logW   E.logW      gap      SE.sim
 [1,] 6.273354 7.317537 1.044183 0.011563071
 [2,] 6.141046 7.189388 1.048342 0.010769264
 [3,] 6.104208 7.105363 1.001155 0.009460871
 [4,] 6.027683 7.047106 1.019423 0.009071434
 [5,] 5.969429 7.012677 1.043248 0.008658388
 [6,] 5.924133 6.983507 1.059374 0.008524205
 [7,] 5.890708 6.958233 1.067525 0.008415261
 [8,] 5.863342 6.936076 1.072734 0.008331378
 [9,] 5.839159 6.916587 1.077427 0.008341695
[10,] 5.810654 6.899180 1.088526 0.008325652
print(gap_stat_km)
Clustering Gap statistic ["clusGap"] from call:
clusGap(x = RouteStats_Pca, FUNcluster = kmeans, K.max = 10,     B = 500, nstart = 25)
B=500 simulated reference sets, k = 1..10; spaceH0="scaledPCA"
 --> Number of clusters (method 'firstSEmax', SE.factor=1): 1
          logW   E.logW      gap      SE.sim
 [1,] 6.273354 7.317537 1.044183 0.011563071
 [2,] 6.141046 7.189388 1.048342 0.010769264
 [3,] 6.104208 7.105363 1.001155 0.009460871
 [4,] 6.027683 7.047106 1.019423 0.009071434
 [5,] 5.969429 7.012677 1.043248 0.008658388
 [6,] 5.924133 6.983507 1.059374 0.008524205
 [7,] 5.890708 6.958233 1.067525 0.008415261
 [8,] 5.863342 6.936076 1.072734 0.008331378
 [9,] 5.839159 6.916587 1.077427 0.008341695
[10,] 5.810654 6.899180 1.088526 0.008325652
# pam version
set.seed(123456789)
gap_stat_pm <- clusGap(RouteStats_Pca,
                       FUN = pam,
                       K.max = 10,
                       B = 500
                      )
Clustering k = 1,2,..., K.max (= 10): .. done
Bootstrapping, b = 1,2,..., B (= 500)  [one "." per sample]:
.................................................. 50 
.................................................. 100 
.................................................. 150 
.................................................. 200 
.................................................. 250 
.................................................. 300 
.................................................. 350 
.................................................. 400 
.................................................. 450 
.................................................. 500 
# Print
print(gap_stat_pm, method = "Tibs2001SEmax")
Clustering Gap statistic ["clusGap"] from call:
clusGap(x = RouteStats_Pca, FUNcluster = pam, K.max = 10, B = 500)
B=500 simulated reference sets, k = 1..10; spaceH0="scaledPCA"
 --> Number of clusters (method 'Tibs2001SEmax', SE.factor=1): 2
          logW   E.logW      gap     SE.sim
 [1,] 6.273354 7.317222 1.043868 0.01161238
 [2,] 6.140024 7.208149 1.068125 0.01495862
 [3,] 6.087381 7.126041 1.038659 0.01414766
 [4,] 6.017737 7.076534 1.058796 0.01469932
 [5,] 5.991660 7.043313 1.051652 0.01203089
 [6,] 5.943989 7.016020 1.072030 0.01149023
 [7,] 5.922939 6.992303 1.069363 0.01153694
 [8,] 5.888683 6.971776 1.083093 0.01124332
 [9,] 5.863290 6.953980 1.090690 0.01107343
[10,] 5.843109 6.937086 1.093977 0.01050172
print(gap_stat_pm)
Clustering Gap statistic ["clusGap"] from call:
clusGap(x = RouteStats_Pca, FUNcluster = pam, K.max = 10, B = 500)
B=500 simulated reference sets, k = 1..10; spaceH0="scaledPCA"
 --> Number of clusters (method 'firstSEmax', SE.factor=1): 2
          logW   E.logW      gap     SE.sim
 [1,] 6.273354 7.317222 1.043868 0.01161238
 [2,] 6.140024 7.208149 1.068125 0.01495862
 [3,] 6.087381 7.126041 1.038659 0.01414766
 [4,] 6.017737 7.076534 1.058796 0.01469932
 [5,] 5.991660 7.043313 1.051652 0.01203089
 [6,] 5.943989 7.016020 1.072030 0.01149023
 [7,] 5.922939 6.992303 1.069363 0.01153694
 [8,] 5.888683 6.971776 1.083093 0.01124332
 [9,] 5.863290 6.953980 1.090690 0.01107343
[10,] 5.843109 6.937086 1.093977 0.01050172
# hierarchical version
set.seed(123456789)
gap_stat_hcut <- clusGap(RouteStats_Pca,
                         FUN = hcut,
                         K.max = 10,
                         B = 500
                        )
Clustering k = 1,2,..., K.max (= 10): .. done
Bootstrapping, b = 1,2,..., B (= 500)  [one "." per sample]:
.................................................. 50 
.................................................. 100 
.................................................. 150 
.................................................. 200 
.................................................. 250 
.................................................. 300 
.................................................. 350 
.................................................. 400 
.................................................. 450 
.................................................. 500 
# Print
print(gap_stat_hcut, method = "Tibs2001SEmax")
Clustering Gap statistic ["clusGap"] from call:
clusGap(x = RouteStats_Pca, FUNcluster = hcut, K.max = 10, B = 500)
B=500 simulated reference sets, k = 1..10; spaceH0="scaledPCA"
 --> Number of clusters (method 'Tibs2001SEmax', SE.factor=1): 1
          logW   E.logW       gap      SE.sim
 [1,] 6.273354 7.317222 1.0438683 0.011612377
 [2,] 6.171268 7.204939 1.0336710 0.012770759
 [3,] 6.138916 7.130456 0.9915393 0.011444429
 [4,] 6.045351 7.075400 1.0300493 0.012108951
 [5,] 5.985764 7.041724 1.0559601 0.011094868
 [6,] 5.943671 7.013110 1.0694384 0.010703021
 [7,] 5.918347 6.988042 1.0696958 0.010276876
 [8,] 5.883665 6.965752 1.0820861 0.010056348
 [9,] 5.856593 6.945803 1.0892106 0.009895617
[10,] 5.833306 6.927759 1.0944528 0.009736810
print(gap_stat_hcut)
Clustering Gap statistic ["clusGap"] from call:
clusGap(x = RouteStats_Pca, FUNcluster = hcut, K.max = 10, B = 500)
B=500 simulated reference sets, k = 1..10; spaceH0="scaledPCA"
 --> Number of clusters (method 'firstSEmax', SE.factor=1): 1
          logW   E.logW       gap      SE.sim
 [1,] 6.273354 7.317222 1.0438683 0.011612377
 [2,] 6.171268 7.204939 1.0336710 0.012770759
 [3,] 6.138916 7.130456 0.9915393 0.011444429
 [4,] 6.045351 7.075400 1.0300493 0.012108951
 [5,] 5.985764 7.041724 1.0559601 0.011094868
 [6,] 5.943671 7.013110 1.0694384 0.010703021
 [7,] 5.918347 6.988042 1.0696958 0.010276876
 [8,] 5.883665 6.965752 1.0820861 0.010056348
 [9,] 5.856593 6.945803 1.0892106 0.009895617
[10,] 5.833306 6.927759 1.0944528 0.009736810
# Plot kmeans
fviz_gap_stat(gap_stat_km, 
              maxSE = list(method = "Tibs2001SEmax")
             )  # 1 cluster

# Plot pam
fviz_gap_stat(gap_stat_pm, 
              maxSE = list(method = "Tibs2001SEmax")
             )  # 2 cluster

# Plot hierarchical
fviz_gap_stat(gap_stat_hcut, 
              maxSE = list(method = "Tibs2001SEmax")
             )  # 1 cluster

Clustering. How many clusters are there?

kmeans method with various different statistics.

# str(iris)
nb <- NbClust(RouteStats_Pca, #scale(iris[ ,-5]),
              distance = "euclidean",
              min.nc = 2,
              max.nc = 15,
              method = "kmeans",
              index = "all"
             )
*** : The Hubert index is a graphical method of determining the number of clusters.
                In the plot of Hubert index, we seek a significant knee that corresponds to a 
                significant increase of the value of the measure i.e the significant peak in Hubert
                index second differences plot. 
 

*** : The D index is a graphical method of determining the number of clusters. 
                In the plot of D index, we seek a significant knee (the significant peak in Dindex
                second differences plot) that corresponds to a significant increase of the value of
                the measure. 
 
******************************************************************* 
* Among all indices:                                                
* 7 proposed 2 as the best number of clusters 
* 2 proposed 3 as the best number of clusters 
* 1 proposed 5 as the best number of clusters 
* 1 proposed 6 as the best number of clusters 
* 1 proposed 7 as the best number of clusters 
* 6 proposed 8 as the best number of clusters 
* 2 proposed 9 as the best number of clusters 
* 1 proposed 13 as the best number of clusters 
* 2 proposed 15 as the best number of clusters 

                   ***** Conclusion *****                            
 
* According to the majority rule, the best number of clusters is  2 
 
 
******************************************************************* 

fviz_nbclust(nb)
Among all indices: 
===================
* 2 proposed  0 as the best number of clusters
* 1 proposed  1 as the best number of clusters
* 7 proposed  2 as the best number of clusters
* 2 proposed  3 as the best number of clusters
* 1 proposed  5 as the best number of clusters
* 1 proposed  6 as the best number of clusters
* 1 proposed  7 as the best number of clusters
* 6 proposed  8 as the best number of clusters
* 2 proposed  9 as the best number of clusters
* 1 proposed  13 as the best number of clusters
* 2 proposed  15 as the best number of clusters

Conclusion
=========================
* According to the majority rule, the best number of clusters is  2 .

Clustering. How many clusters are there?

Hierarchical clustering method. Particularly looking at silhouette statistics.

# Hierarchical clustering, cut in 2 to 15 groups
for(i in 2:15) {
  assign(paste0("HCRes_K", i),
         eclust(RouteStats_Pca,
                "hclust",
                k = i,
                method = "complete",
                graph = FALSE
               )
        )
  
  assign("x",
         get(paste0("HCRes_K", i)
            )
        )
  
  assign(paste0("HCStats_K", i),
         cluster.stats(dist(RouteStats_Scaled,
                            method ="euclidean"
                           ),
                       x$cluster
                      )
        )
  
  assign("y",
         get(paste0("HCStats_K", i)
            )
        )
  
  assign(paste0("HCDend_K", i),
         fviz_dend(x, rect = TRUE, show_labels = FALSE)
        )
  
  assign(paste0("HCSil_K", i),
         fviz_silhouette(x)
        )
  
  assign(paste0("HCSilWidth_K", i),
         as.data.frame(y$clus.avg.silwidths) %>% 
           mutate(KVal = 1:nrow(.)
                 )
        )
  }
replacing previous import by ‘magrittr::%>%’ when loading ‘dendextend’
  cluster size ave.sil.width
1       1  234          0.44
2       2   34          0.10
  cluster size ave.sil.width
1       1  234          0.40
2       2   33          0.18
3       3    1          0.00
  cluster size ave.sil.width
1       1   81          0.32
2       2  153          0.08
3       3   33          0.11
4       4    1          0.00
  cluster size ave.sil.width
1       1   81          0.26
2       2   91          0.14
3       3   62          0.09
4       4   33          0.06
5       5    1          0.00
  cluster size ave.sil.width
1       1   81          0.16
2       2   91          0.08
3       3   10          0.10
4       4   52          0.25
5       5   33          0.06
6       6    1          0.00
  cluster size ave.sil.width
1       1   81          0.16
2       2   91          0.07
3       3   10          0.10
4       4   52          0.25
5       5   29          0.13
6       6    4          0.20
7       7    1          0.00
  cluster size ave.sil.width
1       1   81          0.15
2       2   61          0.06
3       3   10          0.08
4       4   52          0.22
5       5   30          0.16
6       6   29          0.05
7       7    4          0.18
8       8    1          0.00
  cluster size ave.sil.width
1       1   77          0.21
2       2   61          0.06
3       3   10          0.07
4       4   52          0.19
5       5   30          0.16
6       6   29          0.05
7       7    4          0.15
8       8    4          0.18
9       9    1          0.00
   cluster size ave.sil.width
1        1   77          0.21
2        2   61          0.06
3        3    5          0.40
4        4   52          0.19
5        5   30          0.16
6        6   29          0.05
7        7    4          0.14
8        8    5          0.33
9        9    4          0.18
10      10    1          0.00
   cluster size ave.sil.width
1        1   77          0.21
2        2   61          0.06
3        3    5          0.40
4        4   52          0.19
5        5   30          0.16
6        6   28          0.08
7        7    4          0.14
8        8    5          0.33
9        9    4          0.18
10      10    1          0.00
11      11    1          0.00
   cluster size ave.sil.width
1        1   77          0.21
2        2   61          0.06
3        3    5          0.40
4        4   52          0.19
5        5   30          0.15
6        6   14          0.06
7        7    4          0.14
8        8   14          0.25
9        9    5          0.33
10      10    4          0.18
11      11    1          0.00
12      12    1          0.00
   cluster size ave.sil.width
1        1   77          0.20
2        2   34          0.08
3        3   27          0.07
4        4    5          0.40
5        5   52          0.19
6        6   30          0.13
7        7   14          0.06
8        8    4          0.14
9        9   14          0.25
10      10    5          0.33
11      11    4          0.17
12      12    1          0.00
13      13    1          0.00
   cluster size ave.sil.width
1        1   30          0.30
2        2   34          0.08
3        3   27          0.07
4        4    5          0.40
5        5   52          0.04
6        6   30          0.13
7        7   14          0.06
8        8    4          0.11
9        9   47          0.01
10      10   14          0.25
11      11    5          0.33
12      12    4          0.17
13      13    1          0.00
14      14    1          0.00
   cluster size ave.sil.width
1        1   30          0.30
2        2   34          0.07
3        3   27          0.07
4        4    5          0.40
5        5   52          0.04
6        6   30          0.12
7        7    9          0.18
8        8    4          0.11
9        9   47          0.01
10      10   14          0.19
11      11    5          0.17
12      12    5          0.33
13      13    4          0.13
14      14    1          0.00
15      15    1          0.00
HCSilWidth_AllK <- left_join(select(HCSilWidth_K15,
                                    KVal,
                                    `y$clus.avg.silwidths`
                                   ),
                             HCSilWidth_K14,
                             by = c("KVal" = "KVal")
                            ) %>% 
  left_join(.,
            HCSilWidth_K13,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K12,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K11,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K10,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K9,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K8,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K7,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K6,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K5,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K4,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K3,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K2,
            by = c("KVal" = "KVal")
           )
  
colnames(HCSilWidth_AllK) <- c("KVal", "K15", "K14", "K13", "K12", "K11", "K10", "K9",
                               "K8", "K7", "K6", "K5", "K4", "K3", "K2"
                              )
# Visualize
HCDend_K2

HCDend_K3

HCDend_K4

HCDend_K5

HCDend_K6

HCDend_K7

HCDend_K8

HCDend_K9

HCDend_K10

HCDend_K11

HCDend_K12

HCDend_K13

HCDend_K14

HCDend_K15

HCSil_K2

HCSil_K3

HCSil_K4

HCSil_K5

HCSil_K6

HCSil_K7

HCSil_K8

HCSil_K9

HCSil_K10

HCSil_K11

HCSil_K12

HCSil_K13

HCSil_K14

HCSil_K15

HCSilWidth_AllK

With Hierarchical Clustering and k=2, these are the routes in each cluster.

HC_K2 <- eclust(RouteStats_Pca,
                "hclust",
                k = 2,
                method = "complete",
                graph = FALSE
               )
str(HC_K2)
List of 12
 $ merge      : int [1:267, 1:2] -92 -56 -165 -73 -117 -108 -134 -217 -102 -168 ...
 $ height     : num [1:267] 0.882 1.01 1.09 1.095 1.188 ...
 $ order      : int [1:268] 202 210 39 160 81 92 103 117 221 177 ...
 $ labels     : chr [1:268] "10A" "10B" "10E" "11Y" ...
 $ method     : chr "ward.D2"
 $ call       : language stats::hclust(d = x, method = hc_method)
 $ dist.method: chr "euclidean"
 $ cluster    : Named int [1:268] 1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "names")= chr [1:268] "10A" "10B" "10E" "11Y" ...
 $ nbclust    : num 2
 $ silinfo    :List of 3
  ..$ widths         :'data.frame': 268 obs. of  3 variables:
  .. ..$ cluster  : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
  .. ..$ neighbor : num [1:268] 2 2 2 2 2 2 2 2 2 2 ...
  .. ..$ sil_width: num [1:268] 0.588 0.585 0.582 0.581 0.578 ...
  ..$ clus.avg.widths: num [1:2] 0.439 0.102
  ..$ avg.width      : num 0.397
 $ size       : int [1:2] 234 34
 $ data       :'data.frame':    268 obs. of  15 variables:
  ..$ PC1 : num [1:268] -2.143 -3.5 0.996 2.215 -1.33 ...
  ..$ PC2 : num [1:268] -0.34 -0.708 2.136 2.78 2.115 ...
  ..$ PC3 : num [1:268] -0.2398 -0.0136 -0.1131 1.8714 0.3826 ...
  ..$ PC4 : num [1:268] -0.441 -0.741 -0.231 -1.692 0.286 ...
  ..$ PC5 : num [1:268] 0.455 0.137 -1.133 -0.39 -0.422 ...
  ..$ PC6 : num [1:268] -0.542 -0.651 0.384 0.335 0.856 ...
  ..$ PC7 : num [1:268] -0.248 -0.457 -1.26 -1.574 0.945 ...
  ..$ PC8 : num [1:268] 0.435 0.373 1.788 0.854 0.745 ...
  ..$ PC9 : num [1:268] -0.334 -0.845 0.458 -0.891 -0.619 ...
  ..$ PC10: num [1:268] 0.207 0.69 -1.068 -0.403 -0.095 ...
  ..$ PC11: num [1:268] -0.72 -0.321 0.904 0.47 0.458 ...
  ..$ PC12: num [1:268] -0.347 -0.6553 0.3871 -0.0855 0.0615 ...
  ..$ PC13: num [1:268] 0.1573 -0.0503 0.0605 0.4991 -0.2338 ...
  ..$ PC14: num [1:268] -0.0734 -0.1431 0.1695 0.0626 0.5568 ...
  ..$ PC15: num [1:268] -0.1296 -0.1389 -0.1039 -0.3495 -0.0226 ...
 - attr(*, "class")= chr [1:3] "hclust" "hcut" "eclust"
HC_K2_Clusters <- as.data.frame(HC_K2$cluster) %>% 
  rename(ClusterNum = `HC_K2$cluster`) %>%
  mutate(BusRoute = rownames(.)
        ) %>% 
  arrange(ClusterNum,
          BusRoute
         )
HC_K2_Clusters
group_by(HC_K2_Clusters,
         ClusterNum
        ) %>% 
  summarise(Cnt = n()
          )

Using kmeans, PAM, and Hierarchical clustering methods, we can say we probably have 2 clusters.

Let’s try density clustering. (This tends to show that maybe there is only one “cluster,” meaning that data are not clusterable.)

rm(list = ls(pattern = "_K")
  )
# Compute DBSCAN using fpc package
kNNdistplot(RouteStats_Pca, k = 10)
abline(h = 8.5, lty = 2)

set.seed(123456789)
db <- fpc::dbscan(RouteStats_Pca,
                  eps = 8.5,
                  MinPts = 10
                )
str(db)
List of 4
 $ cluster: num [1:268] 1 1 1 1 1 1 1 1 1 1 ...
 $ eps    : num 8.5
 $ MinPts : num 10
 $ isseed : logi [1:268] TRUE TRUE TRUE TRUE TRUE TRUE ...
 - attr(*, "class")= chr "dbscan"
db
dbscan Pts=268 MinPts=10 eps=8.5
       0   1
border 5   7
seed   0 256
total  5 263
# Plot DBSCAN results
fviz_cluster(db,
             RouteStats_Pca,
             stand = FALSE,
             frame = FALSE,
             geom = "point"
            )
argument frame is deprecated; please use ellipse instead.

We can say that MAYBE there are two clusters, but there is more evidence for probably just one cluster (i.e., the data are NOT clusterable).

# remove no longer needed items
rm(X2_Long, X2_Pct, ClustData_Ends, db, gap_stat, gap_stat_hcut, gap_stat_km, gap_stat_pm, i, nb, rd, Trnsfrm, x, y, BusRoute, Rte, map, WaitTime_AllBus_Zip_Box, WaitTime_AllBus_Zip_Violin, X2_WaitByHr_Line)
rm(list = ls(pattern = "Count")
  )
rm(list = ls(pattern = "RouteStop_")
  )
rm(list = ls(pattern = "TimeBtw")
  )
rm(list = ls(pattern = "PcaRes")
  )
rm(BasePath)

Investigating TravelTime_Sec.


View(filter(TTLargeRteChng,
            !is.na(TravelTime_Sec) &
              RteChange2 == "Same"
           ) %>% 
       arrange(desc(TravelTime_Sec),
               SpeedAvg_Mph_NewHvrs
              ) %>%
       head(500)
    )


# examples where TravelTime_Sec is small (1 sec) and SpeedAvg_Mph_NewHvrs is large.
View(select(NewTravTime,
            # -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
            -(TD_Mi_q2:TD_Mi_SSHG_Cnt_F),
            -(TT_Hr_q2:TT_Hr_SSHG_Cnt_F)
           ) %>% 
       filter((RowNum_OG >= 2217353 & RowNum_OG <= 2217373) | # 2217363
                (RowNum_OG >= 3090321 & RowNum_OG <= 3090341) | # 3090331
                (RowNum_OG >= 80764 & RowNum_OG <= 80784) | # 80774
                (RowNum_OG >= 33840 & RowNum_OG <= 33860) # 33850
           )
    )






# examples where TravelTime_Sec is large and SpeedAvg_Mph_NewHvrs is small.
View(filter(TTLargeRteChng,
            (RowNum_OG >= 2250290 & RowNum_OG <= 2250310) | # 2250300
              (RowNum_OG >= 867717 & RowNum_OG <= 867737) | # 867727
              (RowNum_OG >= 864379 & RowNum_OG <= 864399) | # 864389
              (RowNum_OG >= 808395 & RowNum_OG <= 808415) # 808405
           )
    )

         
         
# examples where TravelTime_Sec is unusually small (with TravelDistance_Mi values that are large).
View(filter(AllDays_NewTravelDist,
            (RowNum_OG >= 1042228 & RowNum_OG <= 1042248) | # 1042238
                (RowNum_OG >= 53816 & RowNum_OG <= 53836) | # 53826
                (RowNum_OG >= 360571 & RowNum_OG <= 360591) | # 360581
                (RowNum_OG >= 502271 & RowNum_OG <= 502291) # 502281 (can't explian the weird TravelTime_Sec calculation here - it's not even an integer!)
           )
    )

# still trying to explain 502281...on the day of this weirdness, the bus was only in circulation for 4-5 stops (~20 minutes) on that day (Oct 6)
View(filter(AllDays_NewTravelDist,
            Bus_ID == 2711
           )
    )


# exploring large values for TravelTime_Sec
View(filter(AllDays_NewTravelDist,
            TravelTime_Sec == 300
           ) %>% 
       arrange(desc(TravelTime_Sec),
               SpeedAvg_Mph2
              )
    )

# examples where TravelTime_Sec is unusually large (with TravelDistance_Mi values that are small, so SpeedAvg_Mph values are very small).
View(filter(AllDays_NewTravelDist,
            (RowNum_OG >= 2627459 & RowNum_OG <= 2627479) | # 2627469
                (RowNum_OG >= 2193344 & RowNum_OG <= 2193364) | # 2193354
                (RowNum_OG >= 1644123 & RowNum_OG <= 1644143) | # 1644133
                (RowNum_OG >= 869600 & RowNum_OG <= 869620) # 869610
           )
    )

Investigation of SpeedAvg_Mph2

View(Speed_Pctiles): 90% of SpeedAvg_Mph2 are between ~3mph and ~66mph.


Speed_Ntile <- as.data.frame(AllDays_NewTravelDist$SpeedAvg_Mph2) %>% 
  mutate(Pctile = ntile(AllDays_NewTravelDist$SpeedAvg_Mph2, 100),
         MinR = min_rank(AllDays_NewTravelDist$SpeedAvg_Mph2),
         PctR = percent_rank(AllDays_NewTravelDist$SpeedAvg_Mph2),
         PctR_Round = round(PctR, 2)
        ) 

colnames(Speed_Ntile)[1] <- "SpeedAvg_Mph2"
str(Speed_Ntile)

Speed_Ntile_Rows <- nrow(Speed_Ntile)

View(tail(Speed_Ntile, 500))


Speed_Pctiles <- group_by(Speed_Ntile,
                          PctR_Round
                         ) %>% 
  summarise(
    MinSpeedAtPctile = min(SpeedAvg_Mph2),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / Speed_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(Speed_Pctiles)

Investigation of SpeedAvg_Mph2.

Exploring the removal of outlier TravelTime_Sec and TravelDistance_Mi.


summary(select(AllDays_NewTravelDist,
               SpeedAvg_Mph,
               SpeedAvg_Mph2
              )
       )

summary(select(filter(AllDays_NewTravelDist,
                      TravelDistance_Mi > 0.0001893939 & # lowest non-zero percentile
                        TravelDistance_Mi < 1.0812500000 & # 99th percentile
                        TravelTime_Sec > 10.050000 & # 2nd percentile
                        TravelTime_Sec < 293.000000 # 98th percentile
                     ),
               SpeedAvg_Mph,
               SpeedAvg_Mph2
              )
       )

Investigation of SpeedAvg_Mph2.

Histogram of SpeedAvg_Mph2.


Speed_HistDen <- ggplot(filter(AllDays_NewTravelDist,
                               !is.na(SpeedAvg_Mph2)
                              ),
                        aes(x = SpeedAvg_Mph2,
                            y = ..density..
                           )
                       ) +
  geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  stat_bin(binwidth = 5,
           geom = "text",
           size = 2.5,
           vjust = 1.5,
           aes(label = format(..count.., big.mark = ",")
              ),
          ) +
  # geom_text(aes(label = format(..count.., big.mark = ",")
  #              ),
  #           size = 3,
  #           nudge_y = (..count.. * 0.1)
  #          ) +
  coord_cartesian(xlim = c(0, 70), ylim = c(0, 0.04)
                 ) +
  #  theme(legend.position="none") +
  labs(title = "Variation in Travel Speed",
       x = "Average Speed (mph)",
       y = "Density"
      )

Speed_HistDen

Investigation of SpeedAvg_Mph2.

Histogram of SpeedAvg_Mph2 after removing outlier TravelTime_Sec and TravelDistance_Mi.


View(TravDistMiNew_Pctiles)
View(TravTimeHr_Pctiles)

SpeedNoOutlier_HistDen <- ggplot(filter(AllDays_NewTravelDist,
                                        !is.na(SpeedAvg_Mph2) &
                                          TravelDistance_Mi_New > 0.077841005 & # 5th percentile
                                          # TravelDistance_Mi_New < 1.0812500000 & # 99th percentile
                                          TravelTime_Sec > 12.100000 # 4th percentile
                                          # TravelTime_Sec < 293.000000 # 98th percentile
                                       ),
                                 aes(x = SpeedAvg_Mph2,
                                     y = ..density..
                                    )
                                ) +
  geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  stat_bin(binwidth = 5,
           geom = "text",
           size = 2.5,
           vjust = 1.5,
           aes(label = format(..count.., big.mark = ",")
              ),
          ) +
  # geom_text(aes(label = format(..count.., big.mark = ",")
  #              ),
  #           size = 3,
  #           nudge_y = (..count.. * 0.1)
  #          ) +
  coord_cartesian(xlim = c(0, 70), ylim = c(0, 0.04)
                 ) +
  #  theme(legend.position="none") +
  labs(title = "Variation in Travel Speed",
       subtitle = "(removed low outliers of Travel Distance and Travel Time)",
       x = "Average Speed (mph)",
       y = "Density"
      )

SpeedNoOutlier_HistDen

Investigation of SpeedAvg_Mph2.

New dataset (NoOutliers_TravelDistNTime) when removing outlier low values of TravelDistance_Mi_New and TravelTime_Sec.


View(TravDistMiNew_Pctiles)
View(TravTimeHr_Pctiles)

NoOutliers_TravelDistNTime <- filter(AllDays_NewTravelDist,
                                     TravelDistance_Mi_New > .077841005 & # 5th percentile
                                       # TravelDistance_Mi_New < 1.0812500000 & # 99th percentile
                                       TravelTime_Sec > 12.100000 # 4th percentile
                                       # TravelTime_Sec < 293.000000 # 98th percentile
                                    )

nrow(AllDays_NewTravelDist) - nrow(NoOutliers_TravelDistNTime)

str(NoOutliers_TravelDistNTime)
summary(NoOutliers_TravelDistNTime)

Investigation of SppedAvg_Mph2.

View(Speed_NoOut_Pctiles): Aproximately 90% of SpeedAvg_Mph2 values are between ~4mph and ~56mph.


Speed_NoOut_Ntile <- as.data.frame(NoOutliers_TravelDistNTime$SpeedAvg_Mph2) %>% 
  mutate(Pctile = ntile(NoOutliers_TravelDistNTime$SpeedAvg_Mph2, 100),
         MinR = min_rank(NoOutliers_TravelDistNTime$SpeedAvg_Mph2),
         PctR = percent_rank(NoOutliers_TravelDistNTime$SpeedAvg_Mph2),
         PctR_Round = round(PctR, 2)
        ) 

colnames(Speed_NoOut_Ntile)[1] <- "SpeedAvg_Mph2"
str(Speed_NoOut_Ntile)

Speed_NoOut_Ntile_Rows <- nrow(Speed_NoOut_Ntile)

View(tail(Speed_NoOut_Ntile, 500))


Speed_NoOut_Pctiles <- group_by(Speed_NoOut_Ntile,
                                PctR_Round
                               ) %>% 
  summarise(
    MinSpeedAtPctile = min(SpeedAvg_Mph2),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / Speed_NoOut_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(Speed_NoOut_Pctiles)

Investigation of SppedAvg_Mph2.

Exloring odd/impossible values.


# Exploring when SpeedAvg_Mph2 is NA  --  does not occur at all
nrow(filter(NoOutliers_TravelDistNTime,
            is.na(SpeedAvg_Mph2)
           )
    )


# Exploring when SpeedAvg_Mph2 is zero  --  does not occur at all
nrow(filter(NoOutliers_TravelDistNTime,
            SpeedAvg_Mph2 == 0
           )
    )


# examples where SpeedAvg_Mph2 < 3.2848770
View(filter(AllDays_NewTravelDist,
            SpeedAvg_Mph2 > 0 &
              SpeedAvg_Mph2 < 3.2848770
           ) %>% 
       arrange(SpeedAvg_Mph2)
    )

# examples where SpeedAvg_Mph2 < 3.2848770
View(filter(AllDays_NewTravelDist,
            (RowNum_OG >= 485338 & RowNum_OG <= 485358) | # 485348  --  Extreme travel time, Route Change
                (RowNum_OG >= 346952 & RowNum_OG <= 346972) | # 346962  -- Extreme travel time, Route Change 
                (RowNum_OG >= 70494 & RowNum_OG <= 70514) | # 70504  --  Extreme travel time, Route Change
                (RowNum_OG >= 2051846 & RowNum_OG <= 2051866) # 2051856  --  Extreme travel time, Route Change
           )
    )

Investigation of SpeedAvg_Mph2.

Limit the dataset based on SpeedAvg_Mph2.


NoOutliersSpeed <- filter(NoOutliers_TravelDistNTime,
                          between(SpeedAvg_Mph2,
                                  4.069300, # 5th percentile
                                  56.05651 #95th percentile
                                 )
                          )

nrow(NoOutliers_TravelDistNTime) - nrow(NoOutliersSpeed)

summary(NoOutliersSpeed)

TravelTime now looks like it has some odd values on the high end. So let’s look at those.

View(TravTime_NoOut_Pctiles): Virtually all trips should take less than 5 minutes. (The 99th percentile of of TravelTime is approximately 8 minutes.)


TravTime_NoOut_Ntile <- as.data.frame(NoOutliersSpeed$TravelTime_Hr) %>% 
  mutate(Pctile = ntile(NoOutliersSpeed$TravelTime_Hr, 100),
         MinR = min_rank(NoOutliersSpeed$TravelTime_Hr),
         PctR = percent_rank(NoOutliersSpeed$TravelTime_Hr),
         PctR_Round = round(PctR, 2)
        )

colnames(TravTime_NoOut_Ntile)[1] <- "TravelTime_Hr"
str(TravTime_NoOut_Ntile)

TravTime_NoOut_Ntile_Rows <- nrow(TravTime_NoOut_Ntile)

View(tail(TravTime_NoOut_Ntile, 500))


TravTime_NoOut_Pctiles <- group_by(TravTime_NoOut_Ntile,
                                   PctR_Round
                                  ) %>% 
  summarise(
    MinTravTimeHrAtPctile = min(TravelTime_Hr),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / TravTime_NoOut_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile),
         MinTravTimeSecAtPctile = MinTravTimeHrAtPctile * (60 * 60)
        )

View(TravTime_NoOut_Pctiles)

Investigating odd TravelTime_Sec values.

Trips longer than ~8 minutes.


View(filter(NoOutliersSpeed,
            TravelTime_Sec > 491 # min at the 100th percentile
           ) %>% 
       arrange(desc(TravelTime_Sec)
              )
    )

# examples of TravelTime_Sec values that are largest.
View(filter(NoOutliersSpeed,
            (RowNum_OG >= 2071759 & RowNum_OG <= 2071779) | # 2071769  --  results from a route change, and a 3hr+ wait before the new route starts
                (RowNum_OG >= 1473686 & RowNum_OG <= 1473706) | # 1473696  --  results from a route change, and a 3hr wait before the new route starts
                (RowNum_OG >= 1222822 & RowNum_OG <= 1222842) | # 1222832  --  results from a route change, and a 3hr wait before the new route starts
                (RowNum_OG >= 3046089 & RowNum_OG <= 3046109) # 3046099  --  results from a route change, and a 3hr wait before the new route starts
           )
    )


# examples of TravelTime_Sec values that are the smallest of the large.
View(filter(NoOutliersSpeed,
            (RowNum_OG >= 3044689 & RowNum_OG <= 3044709) | # 3044699  --  results from a route change
                (RowNum_OG >= 3022358 & RowNum_OG <= 3022378) | # 3022368  --  results from a route change
                (RowNum_OG >= 2993016 & RowNum_OG <= 2993036) | # 2993026  --  results from a previous route change (change occurred in deleted row)
                (RowNum_OG >= 2683703 & RowNum_OG <= 2683723) # 2683713  --  results from a previous route change (change occurred in deleted row)
           )
    )

Let’s look at the TravelTime_Sec values and route changes (DirChange2).

The 99th percentile of TravelTime_Sec for both, all trips, and just those trips NOT involving route changes (DirChange2 = “Same”), is approximately 5min (300 sec).

Nota Bene: The percentile calculation here is defined slightly different than in most of the above analyses (which get the lowest value in the bin created by 100 ntiles).


summary(select(NoOutliersSpeed,
               TravelTime_Sec
              )
       )

summary(select(filter(NoOutliersSpeed,
                      DirChange2 == "Same"
                     ),
               TravelTime_Sec
              )
       )

summary(select(filter(NoOutliersSpeed,
                      DirChange2 == "Change"
                     ),
               TravelTime_Sec
              )
       )


TravTimeSec_Qtiles_df <- data.frame(PctValue = seq(0, 100, 1),
                                    All = seq(1, 101, 1),
                                    Same = seq(1, 101, 1),
                                    Change = seq(1, 101, 1)
                                   )

TravTimeSec_Qtiles_df[ , 2] <- quantile(select(NoOutliersSpeed,
                                               TravelTime_Sec
                                              ),
                                        probs = seq(0, 1, 0.01),
                                        na.rm = TRUE
                                       )

TravTimeSec_Qtiles_df[ , 3] <- quantile(select(filter(NoOutliersSpeed,
                                                      DirChange2 == "Same"
                                                     ),
                                               TravelTime_Sec
                                              ),
                                        probs = seq(0, 1, 0.01),
                                        na.rm = TRUE
                                       )

TravTimeSec_Qtiles_df[ , 4] <- quantile(select(filter(NoOutliersSpeed,
                                                      DirChange2 == "Change"
                                                     ),
                                               TravelTime_Sec
                                              ),
                                        probs = seq(0, 1, 0.01),
                                        na.rm = TRUE
                                       )

View(TravTimeSec_Qtiles_df)

Limit the dataset now based on TravelTime_Sec.


UpperLimitTravTime <- filter(NoOutliersSpeed,
                             TravelTime_Sec <= 491 # min at the 100th percentile
                             )

nrow(NoOutliersSpeed) - nrow(UpperLimitTravTime)

str(UpperLimitTravTime)

summary(UpperLimitTravTime)

Investigation of Dwell_Time2 (how long the bus is at a stop).

Differences between Dwell_Time (by WMATA) and Dwell_Time2 (by me) appear to be due to switches in RouteAlt. WMATA calculates Dwell_Time by an unknown process. The WMATA calculation is equal to my calculation, except for the records immedaitely before and after a RouteAlt switch (DirChange2).


View(filter(AllDays_NewOrder,
            Dwell_Time != Dwell_Time2
           )
    )


# Examples where the Dwell_Time and Dwell_Time2 are different
View(filter(AllDays_NewOrder,
            ( (RowNum_OG >= 65 & RowNum_OG <= 85) | # 75
                (RowNum_OG >= 162 & RowNum_OG <= 192) | # 172
                (RowNum_OG >= 431952 & RowNum_OG <= 431972) | # 431962
                (RowNum_OG >= 434595 & RowNum_OG <= 434615) # 434605  --  this record is NOT a route switch, but does has a Sequence switch (Me: should there really be a route switch here?)
            )
           )
    )

Investigation of Dwell_Time2 (how long the bus is at a stop).

First, create some “rank” stats. View(DT2_Pctiles): 95% of Dwell_Time2s are <= 23 seconds…but some weird (e.g., nearly 2 hour Dwell_Time2s exist).


DwellTime2_Ntile <- as.data.frame(AllDays_NewOrder$Dwell_Time2) %>% 
  mutate(Pctile = ntile(AllDays_NewOrder$Dwell_Time2, 100),
         MinR = min_rank(AllDays_NewOrder$Dwell_Time2),
         PctR = percent_rank(AllDays_NewOrder$Dwell_Time2),
         PctR_Round = round(PctR, 2)
        ) 

colnames(DwellTime2_Ntile)[1] <- "Dwell_Time2"
str(DwellTime2_Ntile)

DwellTime2_Ntile_Rows <- nrow(DwellTime2_Ntile)

View(tail(DwellTime2_Ntile, 500))


DwellTime2_Pctiles <- group_by(DwellTime2_Ntile,
                               PctR_Round
                              ) %>% 
  summarise(
    MinDwellAtPctile = min(Dwell_Time2),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / DwellTime2_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(DwellTime2_Pctiles)

Investigation of Dwell_Time2 (how long the bus is at a stop).

Histogram of Dwell_Time2.


DwellTime2_HistDen <- ggplot(AllDays_NewOrder, aes(x = Dwell_Time2, y = ..density..)) +
  geom_histogram(binwidth = 1, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  coord_cartesian(xlim = c(1, 25), ylim = c(0, 0.05)
                 ) +
  xlab("Time a Bus Stays at a Stop (sec)") + 
  ylab("Density") + 
  #  theme(legend.position="none") + 
  ggtitle(expression(atop("Variation in How Long a Bus Stays at a Stop"
                          # ,atop(italic("xxxxx"),"")
                         )
                    )
         )

DwellTime2_HistDen

Investigation of Dwell_Time2 (how long the bus is at a stop).

Looking at some weirdly long Dwell_Time2 values.


View(arrange(AllDays_NewOrder,
             desc(Dwell_Time2)
            )
    )


# examples of extremely large Dwell_Time2s
View(filter(AllDays_NewOrder,
            (RowNum_OG >= 292669 & RowNum_OG <= 292689) | # 292679
                (RowNum_OG >= 531057 & RowNum_OG <= 531077) | # 531067
                (RowNum_OG >= 1388627 & RowNum_OG <= 1388647) | # 1388637
                (RowNum_OG >= 1645711 & RowNum_OG <= 1645731) # 1645721
           )
    )


View(filter(AllDays_NewOrder,
            Dwell_Time2 == 0
           )
    )

Investigation of Delta_Time (how early or late the bus is).

View(DT2_Pctiles): 94% of Delta_Time values are between -236 seconds and 1,259 seconds. Roughly 66% of records are within 5 min late and 5 min early…but some weird (e.g., almost 50 minute late or 40 minute early) Delta_Times exist.

Note that Delta_Time is the difference from the scheduled bus arrival. So if two buses are scheduled to arrive at a destination at 10:00pm and 10:20pm, and if the 10:20pm bus has a Delta_Time of 5 minutes, there are 25 minutes between bus arrivals at the stop.

Also note that based on a comment at https://planitmetro.com/2016/11/16/data-download-metrobus-vehicle-location-data/, the Delta_Time values don’t appear to coincide with published bus schedules (e.g., the X2 departing every 8 minutes during peak hours).


DeltTime_Ntile <- as.data.frame(AllDays_NewOrder$Delta_Time) %>% 
  mutate(Pctile = ntile(AllDays_NewOrder$Delta_Time, 100),
         MinR = min_rank(AllDays_NewOrder$Delta_Time),
         PctR = percent_rank(AllDays_NewOrder$Delta_Time),
         PctR_Round = round(PctR, 2)
        ) 

colnames(DeltTime_Ntile)[1] <- "Delta_Time"
str(DeltTime_Ntile)

DeltTime_Ntile_Rows <- nrow(DeltTime_Ntile)

View(tail(DeltTime_Ntile, 500))


DeltTime_Pctiles <- group_by(DeltTime_Ntile,
                             PctR_Round
                            ) %>% 
  summarise(
    MinDeltTimeAtPctile = min(Delta_Time),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / DeltTime_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(DeltTime_Pctiles)
DeltTime_Pctiles

# ~66% of rows are between 5 min late and 5 min early
nrow(filter(AllDays_NewOrder,
            Delta_Time >= -300 &
              Delta_Time <= 300
           )
    ) / nrow(AllDays_NewOrder)


# examples of weird large Delta_Times
View(filter(AllDays_NewOrder,
            Delta_Time < -4202 |
              Delta_Time > 1705
           ) %>% 
       arrange(desc(Delta_Time)
              )
    )

Investigation of Delta_Time (how early or late the bus is).

Delta_Time histogram.


DeltTime_HistDen <- ggplot(AllDays_NewOrder, aes(x = (Delta_Time / 60),
                                                 y = ..density..
                                                )
                          ) +
  geom_histogram(binwidth = (5/60), fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  coord_cartesian(xlim = c(-5, 5)) +
  xlab("Bus Lateness (min)") + 
  ylab("Density") + 
  #  theme(legend.position="none") + 
  ggtitle(expression(atop("Variation in How Early/Late a Bus Is",
                          atop(italic("(positive values are late arrivals)"),
                               ""
                              )
                         )
                    )
         )

DeltTime_HistDen

Investigation of Delta_Time (how early or late the bus is).

Delta_Time boxplot.


# Count_Values is needed to display the medians on the box plots
Count_Values <- ddply(AllDays_NewOrder,
                      .(Event_Time_HrGroup),
                      summarise,
                      Value_Counts = median(Delta_Time / 60, na.rm = TRUE)
                     )

DeltTime_BoxPlot <- ggplot(AllDays_NewOrder,
                           aes(factor(Event_Time_HrGroup),
                               Delta_Time / 60,
                               fill = factor(Event_Time_HrGroup)
                              )
                          ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE) + 
  # coord_cartesian(ylim = c(-300, 1200)) +
  coord_cartesian(ylim = c(-5, 20)) +
  geom_text(data = Count_Values,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 3,
            vjust = -0.5
           ) +
  xlab("Hour Group") + 
  ylab("Bus Lateness (minutes)") + 
  theme(legend.position="none", axis.text.x = element_text(angle=45)) + 
  #theme(legend.position="right", axis.text.x = element_blank()) + 
  ggtitle(expression(atop("How Early/Late is the Bus (by Hour Group)",
                          atop(italic("(positive values are late arrivals)"),
                               ""
                              )
                         )
                    )
         )

DeltTime_BoxPlot

Investigation of Delta_Time (how early or late the bus is).

Exploring “extreme” Delta_Times. First let’s get some “rank” stats.


View(DeltTime_Pctiles)
DeltTime_Pctiles


DeltTimeAbs_Ntile <- as.data.frame(abs(AllDays_NewOrder$Delta_Time)) %>% 
  mutate(Pctile = ntile(abs(AllDays_NewOrder$Delta_Time), 100),
         MinR = min_rank(abs(AllDays_NewOrder$Delta_Time)),
         PctR = percent_rank(abs(AllDays_NewOrder$Delta_Time)),
         PctR_Round = round(PctR, 2)
        ) 

colnames(DeltTimeAbs_Ntile)[1] <- "Delta_Time_Abs"
str(DeltTimeAbs_Ntile)

DeltTimeAbs_Ntile_Rows <- nrow(DeltTimeAbs_Ntile)

View(tail(DeltTimeAbs_Ntile, 500))


DeltTimeAbs_Pctiles <- group_by(DeltTimeAbs_Ntile,
                                PctR_Round
                               ) %>% 
  summarise(
    MinDeltTimeAtPctile = min(Delta_Time_Abs),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / DeltTime_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(DeltTimeAbs_Pctiles)
DeltTimeAbs_Pctiles

Investigation of Delta_Time (how early or late the bus is).

Exploring “extreme” Delta_Times. Then let’s calculate the percentage of buses that are 10 minutes (or more) late/early.


HrGroup_DeltaTime_All <- group_by(AllDays_NewOrder,
                                  Event_Time_HrGroup
                                 ) %>% 
  summarise(EventAll_Cnt = n()
           )

str(HrGroup_DeltaTime_All)
View(HrGroup_DeltaTime_All)


HrGroup_DeltaTime_Above10Min <- filter(AllDays_NewOrder,
                                       abs(Delta_Time) >= 600
                                      ) %>% 
  group_by(Event_Time_HrGroup) %>% 
  summarise(EventAbove10_Cnt = n()
           )

str(HrGroup_DeltaTime_Above10Min)
View(HrGroup_DeltaTime_Above10Min)


HrGroup_DeltaTimeCompare <- inner_join(HrGroup_DeltaTime_Above10Min,
                                       HrGroup_DeltaTime_All,
                                       by = c("Event_Time_HrGroup" = "Event_Time_HrGroup")
                                      ) %>% 
  mutate(PctEventsAbove10 = EventAbove10_Cnt / EventAll_Cnt)

View(HrGroup_DeltaTimeCompare)

Investigation of Delta_Time (how early or late the bus is).

Quickly plot these “extreme” Delta_Times.


DeltTime_Above10_Cols <- ggplot(HrGroup_DeltaTimeCompare,
                                aes(factor(Event_Time_HrGroup),
                                    PctEventsAbove10
                                   )
                               ) +
  geom_col(fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_text(aes(label = format(round(PctEventsAbove10, digits = 2),
                               nsmall = 2
                              )
               ),
            size = 3,
            nudge_y = (HrGroup_DeltaTimeCompare$PctEventsAbove10 * -0.1)
           ) +
  # coord_cartesian(xlim = c(-5, 5)) +
  xlab("Hour Group") + 
  ylab("Percent of All Bus Arrivals") +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  ggtitle(expression(atop("When is a Bus 10+ Minutes Late/Early"
                          # ,atop(italic("positive values are late arrivals"),
                          #      ""
                          #     )
                         )
                    )
         )

DeltTime_Above10_Cols

Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Correlation.


DwellTDeltaT_Corr <- as.matrix(cor(x = AllDays_NewOrder$Dwell_Time2,
                                   y = AllDays_NewOrder$Delta_Time,
                                   use = "pairwise"
                                  )
                               )

DwellTDeltaT_Corr

Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Next, let’s get a sample of data for plotting. Let’s do this for the full dataset (AllDays_NewOrder).


AllDays_NewOrder_10PctSamp <- sample_frac(AllDays_NewOrder, 0.1) %>% 
  select(Delta_Time,
         Dwell_Time2
        ) %>% 
  mutate(DataSet = "AllData")

str(AllDays_NewOrder_10PctSamp)

Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Let’s also get a sample of data for plotting, but with a datset that removes outliers.


View(DeltTime_Pctiles)
View(DwellTime2_Pctiles)

AllDays_NewOrder_NoExtremes_10PctSamp <- filter(AllDays_NewOrder,
                                                between(Delta_Time, -402, 1705) & # removes about 2% of Delta_Time values
                                                  between(Dwell_Time2, 1, 63)  # removes about 2% of Dwell_Time2 values
                                               ) %>% 
  sample_frac(0.1) %>% 
  select(Delta_Time,
         Dwell_Time2
        ) %>% 
  mutate(DataSet = "OutliersRemoved")

str(AllDays_NewOrder_NoExtremes_10PctSamp)

Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Plotting the data from the dataset that does not remove outliers.


DwellTDeltaT_Scatter <- ggplot(AllDays_NewOrder_10PctSamp,
                               aes(Dwell_Time2, Delta_Time)
                              ) +
  geom_point(shape = 1, alpha = 0.5) +
  scale_shape(solid = FALSE) +
  geom_smooth(method = "lm", colour = "red") +
  # xlab("Time at Stop (sec)") + 
  # ylab("Lateness (sec)") +
  annotate(label = lm_eqn(df = AllDays_NewOrder_10PctSamp,
                          y = AllDays_NewOrder_10PctSamp$Delta_Time,
                          x = AllDays_NewOrder_10PctSamp$Dwell_Time2
                         ),
           x = 2200,
           y = 600,
           geom = "text",
           size = 3,
           colour = "red",
           parse = TRUE
          ) +
  labs(title = "Lateness vs Time at Stop",
       subtitle = "(no outliers removed)",
       x = "Time at Stop (sec)",
       y = "Lateness (sec)"
      )
  # ggtitle(expression(atop("Lateness vs Time at Stop"
  #                         ,atop(italic("(no outliers removed)"),
  #                               ""
  #                              )
  #                        )
  #                   )
  #        )
# +
#   geom_jitter()

DwellTDeltaT_Scatter

Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Plotting the data from the dataset that does remove outliers.


DwellTDeltaT_Scatter_NoExtremes <- ggplot(AllDays_NewOrder_NoExtremes_10PctSamp,
                                          aes(Dwell_Time2, Delta_Time)
                                         ) +
  geom_point(shape = 1, alpha = 0.5) +
  scale_shape(solid = FALSE) +
  geom_smooth(method = "lm", colour = "blue") +
  # xlab("Time at Stop (sec)") + 
  # ylab("Lateness (sec)") +
  annotate(label = lm_eqn(df = AllDays_NewOrder_NoExtremes_10PctSamp,
                          y = AllDays_NewOrder_NoExtremes_10PctSamp$Delta_Time,
                          x = AllDays_NewOrder_NoExtremes_10PctSamp$Dwell_Time2
                         ),
           x = 50,
           y = -475,
           geom = "text",
           size = 3,
           colour = "blue",
           parse = TRUE
          ) +
  labs(title = "Lateness vs Time at Stop",
       subtitle = "(2% of outliers removed)",
       x = "Time at Stop (sec)",
       y = "Lateness (sec)"
      )
  # ggtitle(expression(atop("Lateness vs Time at Stop"
  #                         ,atop(italic("(2% of outliers removed)"),
  #                               ""
  #                              )
  #                        )
  #                   )
  #        )
# +
#   geom_jitter()

DwellTDeltaT_Scatter_NoExtremes

Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Plotting the data from both datasets together.


CombinedData <- rbind(AllDays_NewOrder_10PctSamp,
                      AllDays_NewOrder_NoExtremes_10PctSamp
                     )

CombinedData$DataSet <- factor(CombinedData$DataSet)

str(CombinedData)


DwellTDeltaT_Scatter_Combined <- ggplot(CombinedData,
                                        aes(x = Dwell_Time2,
                                            y = Delta_Time,
                                            colour = DataSet
                                           )
                                       ) +
  geom_point(shape = 1, alpha = 0.5) +
  scale_shape(solid = FALSE) +
  coord_cartesian(xlim = c(0, 500), ylim = c(-1000, 2000)
                 ) +
  geom_smooth(data = filter(CombinedData,
                            DataSet == "AllData"
                           ),
              method = "lm",
              colour = "red"
             ) +
  geom_smooth(data = filter(CombinedData,
                            DataSet == "OutliersRemoved"
                           ),
              method = "lm",
              colour = "blue"
             ) +
  # facet_wrap( ~ DataSet, ncol = 2) +
  annotate(label = lm_eqn(df = AllDays_NewOrder_10PctSamp,
                          y = AllDays_NewOrder_10PctSamp$Delta_Time,
                          x = AllDays_NewOrder_10PctSamp$Dwell_Time2
                         ),
           x = 300,
           y = -600,
           geom = "text",
           size = 3,
           colour = "red",
           parse = TRUE
          ) +
  annotate(label = lm_eqn(df = AllDays_NewOrder_NoExtremes_10PctSamp,
                          y = AllDays_NewOrder_NoExtremes_10PctSamp$Delta_Time,
                          x = AllDays_NewOrder_NoExtremes_10PctSamp$Dwell_Time2
                         ),
           x = 300,
           y = -800,
           geom = "text",
           size = 3,
           colour = "blue",
           parse = TRUE
          ) +
  theme(legend.position = "bottom") +
  labs(title = "Lateness vs Time at Stop",
       x = "Time at Stop (sec)",
       y = "Lateness (sec)"
      )
  # ggtitle(expression(atop("Lateness vs Time at Stop"
                          # ,atop(italic("2% of outliers removed"),
                          #       ""
                          #      )
         #                 )
         #            )
         # )
# +
#   geom_jitter()

DwellTDeltaT_Scatter_Combined

# rm(AllDays_StopIDNew, lat, LL_Stats, LL_Stats_UnqLatLng, LL_StatsZips, lng, WaitData_DayPull, WaitData_RoutePull, Zip, Zips_All, Zips0, Zips1, Zips2, Zips3, Zips4, Zips5, Zips6, Zips7, Zips8, Zips9, Zips10, APIData1, BasePath, i, k, pages1, url_1, url_2, url_3, username)
---
title: "R Notebook for WMATA Metrobus Analyses"
output:
  html_notebook: default
  html_document: default
---
   
   This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook for analysis using data on the DC Bus System (WMATA Metrobus).  The data were obtained here:   
[https://planitmetro.com/2016/11/16/data-download-metrobus-vehicle-location-data/](https://planitmetro.com/2016/11/16/data-download-metrobus-vehicle-location-data/)  


These analyses coincide with a Shiny dashboard on waitimes found here:   
[https://mdat.shinyapps.io/DCMetroBus_WaitTimes_20170319/](https://mdat.shinyapps.io/DCMetroBus_WaitTimes_20170319/)  
  

   Load the packages to be used.
```{r message = FALSE, warning = FALSE}

# install.packages('rgeos', type='source')
# install.packages('rgdal', type='source')
# install.packages("NbClust")

library("jsonlite")           # manipulating JSON files for zip codes
library("sqldf")              # sql-based data manipulation
library("tcltk")
library("tidyr")              # data manipulation
library("plyr")               # data manipulation
library("dplyr")              # data manipulation
library("magrittr")           # data manipulation (piping data)
library("stringr")            # string manipulation
library("data.table")         # used in testing data manipulation for speed increases
library("lubridate")          # date manipulation
library("geosphere")          # calculating Haversine distance
library("ggplot2")            # general plotting
library("ggvis")              # general plotting
library("rbokeh")             # general plotting
library("ggmap")              # general plotting of maps
library("rgdal")              # used in plotting shapefiles
library("broom")              # used in plotting shapefiles
library("maptools")           # used in plotting shapefiles
library("rgeos")              # used in plotting shapefiles
library("caret")              # used in PCA
library("cluster")            # used for clustering
library("fpc")                # used for clustering
library("dbscan")             # used for clustering
library("NbClust")            # used for clustering
library("factoextra")         # plotting clusters

```


  Session Info.
  
```{r}

sessionInfo()
```
  
    
    Get the Bus data.

First let's update the directory for this Chunk to the location where the raw data files are saved.
```{r, message=TRUE, warning=TRUE, include=FALSE}

getwd()

```


Then, actually get the data.
```{r warning = FALSE}

setwd(paste0(BasePath, "DCMetroBus/Bus AVL Oct 2016")
     )

for (i in 3:7){
  assign(paste0("Oct0", i, "Raw"),
         read.delim(paste0("2016100", i, "MetrobusAVL.txt"),
                    sep = "\t",
                    header = TRUE,
                    na.strings = NULL
                   )
        )
  
  message("Oct0", i, "Raw")
 
  str(get(paste0("Oct0", i, "Raw")
         )
     )
  }

```


Put the daily data together.
```{r message = FALSE, warning = FALSE}

AllDays <- bind_rows(list(Oct03Raw, Oct04Raw, Oct05Raw, Oct06Raw, Oct07Raw),
                     .id = c("group")
                    )
# dim(AllDays)
str(AllDays)

```


Deleting old data frames.
```{r}

for (i in 3:7){
  rm(list = ls(pattern = paste0("Oct0", i, "Raw")
              )
    )
  
  message("Deleting Oct0", i, "Raw")
  }

```


Updating variable types.

Then, sorting the data and adding a RowNumber (to be used for identifying rows later in the analyses.)
```{r}

rm(i)


AllDays$group <- factor(AllDays$group)
AllDays$Route_Direction <- factor(AllDays$Route_Direction)
AllDays$Event_Time <- as.POSIXct(AllDays$Event_Time, format = "%m-%d-%y %I:%M:%S %p")
AllDays$Departure_Time <- as.POSIXct(AllDays$Departure_Time, format = "%m-%d-%y %I:%M:%S %p")

str(AllDays)


AllDays_Sorted <- arrange(AllDays,
                          Bus_ID,
                          Event_Time
                         ) %>% 
  mutate(RowNum_OG = row_number() # this is useful in identify the row later on
        )

rm(AllDays)
str(AllDays_Sorted)

# View(head(AllDays_Sorted, 100))

```


Inspecting the values of Stop_ID, and finding that it can take the values "" (blank) and "NULL".
```{r}

View(group_by(AllDays_Sorted,
              Stop_ID
             ) %>% 
       summarise(
         Cnt = n()
         ) %>% 
       arrange(Stop_ID)
    )

View(filter(AllDays_Sorted,
            is.na(Stop_ID) |
              Stop_ID == "" |
              Stop_ID == "NULL"
           ) %>% 
       arrange(Stop_Desc)
    )

```


Creating a table of distinct Stop_Desc values when Stop_ID is "" (blank) or "NULL".
```{r}

StopID_New <- filter(AllDays_Sorted,
                     is.na(Stop_ID) |
                       Stop_ID == "" |
                       Stop_ID == "NULL"
                    ) %>% 
  select(Stop_ID, Stop_Desc) %>% 
  distinct() %>% 
  arrange(Stop_ID, Stop_Desc) %>% 
  mutate(StopID_New = 1:nrow(.)
        )

View(StopID_New)
StopID_New

```


Creating a full updated table by filling in StopID_New for when Stop_ID is "" (blank) or NULL.
```{r}

AllDays_StopIDNew <- left_join(AllDays_Sorted,
                               select(StopID_New,
                                      Stop_Desc,
                                      StopID_New
                                     ),
                               by = c("Stop_Desc" = "Stop_Desc")
                              ) %>% 
  mutate(StopID_Clean = ifelse(is.na(StopID_New),
                               Stop_ID,
                               StopID_New
                              ),
         StopID_Indicator = factor(ifelse(is.na(StopID_New),
                                          "ID_OK",
                                          "ID_Bad"
                                         )
                                  )
        )

rm(StopID_New)
rm(AllDays_Sorted)
str(AllDays_StopIDNew)

# View(tail(AllDays_StopIDNew, 500))
# View(filter(AllDays_StopIDNew,
#             Stop_Desc == "METROWAY ANNNOUCEMNT CORR"
#            )
#     )

```


Lat Long stats for pulling in Zip codes later.
```{r}

LL_Stats <- group_by(AllDays_StopIDNew,
                     StopID_Clean
                    ) %>% 
  summarise(Lat_Mean = mean(Latitude, na.rm = TRUE),
            Lat_Med = median(Latitude, na.rm = TRUE),
            Lng_Mean = mean(Longitude, na.rm = TRUE),
            Lng_Med = median(Longitude, na.rm = TRUE)
           ) %>% 
  mutate(Lat_MeaLessMed = Lat_Mean - Lat_Med,
         Lng_MeaLessMed = Lng_Mean - Lng_Med,
         RowNum = row_number()
        )

str(LL_Stats)
summary(LL_Stats)

View(head(arrange(LL_Stats,
                  Lat_MeaLessMed
                 ),
          500
         )
    )

View(head(arrange(LL_Stats,
                  desc(Lat_MeaLessMed)
                 ),
          500
         )
    )

View(head(arrange(LL_Stats,
                  Lng_MeaLessMed
                 ),
          500
         )
    )


  head(arrange(LL_Stats,
                  desc(Lng_MeaLessMed)
                 ),
          500
         )

```


Pulling in Zip Code data from api.geonames.org.

Need to group in bunches as [http://api.geonames.org](http://api.geonames.org) limits pulls to ~2000 per hour.
```{r message=FALSE, warning=FALSE}

# URL EXAMPLE:
# http://api.geonames.org/findNearbyPostalCodesJSON?lat=38.89560&lng=-76.94873&radius=0&username=supermdat

url_1 <- "http://api.geonames.org/findNearbyPostalCodesJSON?lat="
url_2 <- "&lng="
url_3 <- "&radius=0&username="
username <- "supermdat"


# need to group in bunches as http://api.geonames.org limits pulls to ~2000 per hour

for(k in 0:10){
##### Store everything in multiple lists
pages1 <- list()


# system.time(

for(i in 1:1000){
  lat <- filter(LL_Stats,
                RowNum == i
               ) %>%
    select(Lat_Med)
  
  lng <- filter(LL_Stats,
                RowNum == i
               ) %>%
    select(Lng_Med)
  
  APIData1 <- fromJSON(paste0(url_1,
                              lat,
                              url_2,
                              lng,
                              url_3,
                              username
                             ),
                       flatten = TRUE
                      )
  
  message("Retrieving Zip Code ", k, "_", i)
  
  pages1[[i]] <- APIData1$postalCodes
  
}
# )


##### Combine the lists into one page
assign(paste0("Zips", k),
       rbind.pages(pages1[sapply(pages1, length) > 0])
      )

Sys.sleep(4500)

}


##### Combine all pages
Zips_All <- bind_rows(Zips0,
                      Zips1,
                      Zips2,
                      Zips3,
                      Zips4,
                      Zips5,
                      Zips6,
                      Zips7,
                      Zips8,
                      Zips9,
                      Zips10,
                      .id = "id"
                     ) %>% 
  mutate(UniqueLatLng = paste(lat, lng, sep = "__")
        )

# str(Zips_All)
# View(head(Zips_All))

# saveRDS(Zips_All, "Zips_All")

```


Reading in the saved Zips_All file.  This is only done when re-running the code to avoid the delay in getting data from [http://api.geonames.org](http://api.geonames.org)
```{r}

Zips_All <- readRDS("Zips_All")

str(Zips_All)

```


Pulling in Zip Code data from api.geonames.org.

Linking the Zip Code data to LL_Stats (the unique Stop_Id-LatLong data).
```{r}


# str(LL_Stats)
LL_Stats_UnqLatLng <- mutate(LL_Stats,
                             UniqueLatLng = paste(Lat_Med, Lng_Med, sep = "__")
                            )

# str(LL_Stats_UnqLatLng)
# View(head(LL_Stats_UnqLatLng))


LL_StatsZips <- left_join(LL_Stats_UnqLatLng,
                          Zips_All,
                          by = c("UniqueLatLng" = "UniqueLatLng")
                         )

str(LL_StatsZips)
# View(head(LL_StatsZips))

# Not sure whey these couldn't be found (why they're NA)
View(filter(LL_StatsZips,
            is.na(postalCode)
           )
    )

```


Join to create one dataset that also includes Zip variables.
```{r}

rm(url_1, url_2, url_3, username, pages0, pages1, pages2, pages3, pages4, pages5, pages6, pages7, pages8, pages9, pages10, i, lat, lng, APIData0, APIData1, APIData2, APIData3, APIData4, APIData5, APIData6, APIData7, APIData8, APIData9, APIData10, LL_Stats, LL_Stats_UnqLatLng)


AllDays_Zips <- left_join(AllDays_StopIDNew,
                          LL_StatsZips,
                          by = c("StopID_Clean" = "StopID_Clean")
                         ) %>% 
  rename(Stop_State = adminCode1,
         Stop_County = adminName2,
         Stop_City = placeName,
         Stop_Zip = postalCode
         )

rm(AllDays_StopIDNew, LL_StatsZips)
str(AllDays_Zips)

```


Updating variable types.
```{r}

AllDays_Zips$Stop_State <- factor(AllDays_Zips$Stop_State)
AllDays_Zips$Stop_County <- factor(AllDays_Zips$Stop_County)
AllDays_Zips$Stop_Zip <- factor(AllDays_Zips$Stop_Zip)
AllDays_Zips$Stop_City <- factor(AllDays_Zips$Stop_City)

AllDays_Zips$distance <- as.numeric(AllDays_Zips$distance)
AllDays_Zips$countryCode <- factor(AllDays_Zips$countryCode)
AllDays_Zips$adminName1 <- factor(AllDays_Zips$adminName1)

str(AllDays_Zips)

```


Feature engineering.

Inspecting incidences of consecutive Stop_IDs. This is done because investigation showed that many conseutive events occurr at the same Stop_ID, but with various Dwell_Times, Odometer_Distances, etc.  All of which affect calculations and analyses.

Create data on the runs (consecutive Stop_IDs).
```{r}

StopID_Runs <- rle(AllDays_Zips$StopID_Clean)

StopID_Runs$ends <- cumsum(StopID_Runs$lengths)

StopID_Runs$starts <- ifelse(is.na(lag(StopID_Runs$ends)
                                  ),
                             1,
                             lag(StopID_Runs$ends) + 1
                            )

str(StopID_Runs)
# class(StopID_Runs)
# 
# StopID_Runs_df <- data.frame(unclass(StopID_Runs))
# str(StopID_Runs_df)
# class(StopID_Runs_df)
# rm(StopID_Runs_df)

```


Trying to link data on RunsGroups with the original data (AllDays_Sorted). The goal is to select only one record per RunsGroup - that being the record with the longest Dwell_Time.

I attempted this computation using both data.frames (dplyr) and data.tables (data.table). However, with 2,809,062 rows in one dataset and 3,119,443 rows in the other dataset, the current computation time is over 5 days...so I'm trying a different strategy to only select the first record in a run.
```{r}

# Create a RunsGroup variable for each run
# StopID_Runs_df$RunsGroup <- paste0("g", seq(1:nrow(StopID_Runs_df)
#                                            )
#                                   )
# 
# str(StopID_Runs_df)
# head(StopID_Runs_df, 25)
# tail(StopID_Runs_df, 25)
# 
# StopID_Runs_df <- StopID_Runs_df %>% 
#   mutate(RowNum = row_number()
#         )
# 
# str(StopID_Runs_df)
# head(StopID_Runs_df, 25)
# tail(StopID_Runs_df, 25)
# 
# 
# # Converting to data.tables for, hopefully, improved performance (speed) in computation
# StopID_Runs_dt <- data.table(StopID_Runs_df)
# setkey(StopID_Runs_dt, RowNum)
# str(StopID_Runs_dt)
# 
# AllDays_Sorted_dt <- data.table(AllDays_Sorted)
# setkey(AllDays_Sorted_dt, RowNum_OG)
# str(AllDays_Sorted_dt)
# # rm(AllDays_Sorted_dt)
# 
# 
# # Actual loop to perform the computations and link to original data (AllDays_Sorted_dt)
# GroupData <- list()
# for(i in 1:nrow(StopID_Runs_dt)
#    ) {
#   assign(paste0("group_", i),
#            StopID_Runs_dt[RowNum == i, RunsGroup]
#           )
# 
#     #####  The code below is the same code as above, but done with dplyr  #####
# 
#     # assign(paste0("group_", i),
#   #        filter(StopID_Runs_df,
#   #               RowNum == i
#   #              ) %>% 
#   #          select(RunsGroup)
#   #       )
# 
#   assign(paste0("group_", i, "_start"),
#          StopID_Runs_dt[RowNum == i, starts]
#         )
# 
#   assign(paste0("group_", i, "_end"),
#          StopID_Runs_dt[RowNum == i, ends]
#         )
# 
#   assign(paste0("group_", i, "_rows"),
#          AllDays_Sorted_dt[RowNum_OG >= as.numeric(get(paste0("group_", i, "_start")
#                                                       )
#                                                   ) &
#                            RowNum_OG <= as.numeric(get(paste0("group_", i, "_end")
#                                                       )
#                                                   ),
#                            RunsGroup := as.character(get(paste0("group_", i)
#                                                         )
#                                                     )
#                           ]
# 
#     #####  The code below is the same as the code above, but done with dplyr  #####
# 
#          # filter(AllDays_Sorted,
#          #        between(RowNum_OG,
#          #                as.numeric(get(paste0("group_", i, "_start")
#          #                              )
#          #                          ),
#          #                as.numeric(get(paste0("group_", i, "_end")
#          #                              )
#          #                          )
#          #               )
#          #       ) %>% 
#          #   mutate(RunsGroup = as.character(get(paste0("group_", i)
#          #                                     )
#          #                                 )
#          #        )
#         )
# 
#   GroupData[[i]] <- get(paste0("group_", i, "_rows"))
# 
#   message("Processing Group ", i, " of 2,809,062")
# }
# 
# 
# GroupData_df <- rbind.fill(GroupData)
# str(GroupData_df)
# head(GroupData_df)
# tail(GroupData_df)
# # rm(GroupData_df)
# 
# 
# group_1
# group_1_start
# group_1_end
# group_1_rows
# group_2_rows
# group_3_rows
# group_50_rows
# str(group_50_rows)
# group_2809062_rows
# GroupData[[1]]
# GroupData[[50]]
# 
# 
# #####  Testing Area (Below)  #####
# #####  Testing Area (Below)  #####
# #####  Testing Area (Below)  #####
# 
# # head(StopID_Runs$starts, 20)
# # head(AllDays_NewOrder$Stop_ID, 20)
# # 
# # 
# # dat <- as.data.frame(c(1,1,7,7,7,9,6,8,2,2,2,1,1,1,1,1))
# # colnames(dat)[1] <- "dat"
# # r <- rle(dat$dat)
# # dat$run <- rep(r$lengths, r$lengths)
# # dat$runLag <- lag(dat$run)
# # dat$cond <- rep(r$values, r$lengths)
# # dat
# # View(dat)

```


When consecutive Stop_ID occurrs, only take the first occurrence. This is done because the computation time to select only the record with the longest Dwell_Time for each run was too long (over 5 days).

This is probably less than ideal with regards to Dwell_Time, but should not make much difference for calculations of travel time, speed, etc.
```{r}

AllDays_FirstStopID <- AllDays_Zips[StopID_Runs$starts, ]

dim(AllDays_Zips)
dim(AllDays_FirstStopID)

nrow(AllDays_Zips) - nrow(AllDays_FirstStopID)

rm(AllDays_Zips, StopID_Runs)
str(AllDays_FirstStopID)

```


Feature engineering.

Creating new variables.
```{r}

AllDays_AddVars <- mutate(AllDays_FirstStopID,
                          Odometer_Distance_Mi = Odometer_Distance / 5280, #5,280 feet in 1 mile
                          Dwell_Time2 = as.numeric(Departure_Time - Event_Time),
                          Event_Time_Yr = as.integer(year(Event_Time)),
                          Event_Time_Mth = as.integer(month(Event_Time)),
                          Event_Time_Date = day(Event_Time),
                          Event_Time_Day = wday(Event_Time, label = TRUE),
                          Event_Time_Hr = hour(Event_Time),
                          Event_Time_Min = minute(Event_Time),
                          Event_Time_HrGroup = factor(ifelse(Event_Time_Hr < 3,
                                                             "Group0_2",
                                                      ifelse(Event_Time_Hr < 6,
                                                             "Group3_5",
                                                      ifelse(Event_Time_Hr < 9,
                                                             "Group6_8",
                                                      ifelse(Event_Time_Hr < 12,
                                                             "Group9_11",
                                                      ifelse(Event_Time_Hr < 15,
                                                             "Group12_14",
                                                      ifelse(Event_Time_Hr < 18,
                                                             "Group15_17",
                                                      ifelse(Event_Time_Hr < 21,
                                                             "Group18_20",
                                                      ifelse(Event_Time_Hr < 24,
                                                             "Group21_23"
                                                            )))))))),
                                                         levels = c("Group0_2",
                                                                    "Group3_5",
                                                                    "Group6_8",
                                                                    "Group9_11",
                                                                    "Group12_14",
                                                                    "Group15_17",
                                                                    "Group18_20",
                                                                    "Group21_23"
                                                                   ),
                                                         ordered = TRUE
                                                     )
                         )

rm(AllDays_FirstStopID)
str(AllDays_AddVars)

```


Function for calculating the distance traveled based on the Haversine formula.  Original code from: https://www.r-bloggers.com/great-circle-distance-calculations-in-r/
```{r}

# Calculates the geodesic distance between two points specified by radian latitude/longitude using the Haversine formula (hf)
# gcd.hf <- function(long1, lat1, long2, lat2) {
#   R <- 6371 # Earth mean radius [km]
#   delta.long <- (long2 - long1)
#   delta.lat <- (lat2 - lat1)
#   a <- sin(delta.lat/2)^2 + cos(lat1) * cos(lat2) * sin(delta.long/2)^2
#   c <- 2 * asin(min(1,sqrt(a)))
#   d = R * c * 0.621371 # 1 km = 0.621371 miles
#   return(d) # Distance in miles
# }

```


Feature engineering.

Creating more variables. Creating a BusEvent row number for future identification purposes. Then, creating various variables to analyze distance traveled and speed.
```{r}

AllDays_BusDay <- group_by(AllDays_AddVars,
                           Bus_ID,
                           Event_Time_Date
                          ) %>% 
  mutate(BusDay_EventNum = row_number(),  # used to identify Bus movements on a particular date
         
         Route_Lag1 = lag(Route),  # used in future analyses to identify Route changes
         RouteAlt_Lag1 = lag(RouteAlt),  # used in future analyses to identify RouteAlt (direction) changes
         
         Odometer_Distance_Lag1 = lag(Odometer_Distance),
         
         Latitude_L1 = lag(Latitude),
         Longitude_L1 = lag(Longitude),
         # Lat_Radian = Latitude*pi/180,
         # Long_Radian = Longitude*pi/180,
         # Lat_Radian_L1 = lag(Lat_Radian),
         # Long_Radian_L1 = lag(Long_Radian),
         
         # accounting for potential negative distances
         TravelDistance_Ft = ifelse(Odometer_Distance > Odometer_Distance_Lag1,
                                    Odometer_Distance - Odometer_Distance_Lag1,
                                    NA
                                   ),
         TravelDistance_Mi = TravelDistance_Ft / 5280, #5,280 feet in 1 mile
         
         # TravelDistance_Mi2 = gcd.hf(long1 = Long_Radian_L1,
         #                             lat1 = Lat_Radian_L1,
         #                             long2 = Long_Radian,
         #                             lat2 = Lat_Radian
         #                            ),
         
         TravelDistance_Mi_Hvrs = 
                              # ifelse((is.na(Longitude_L1) | is.na(Latitude_L1)
                              #        ),
                              #        NA,
                              distHaversine(cbind(Longitude_L1, Latitude_L1),
                                            cbind(Longitude, Latitude)
                                           ) * 0.000621371, # 0.000621371 miles = 1 meter
         
         # accounting for potential negative times
         TravelTime_Sec = as.numeric(ifelse(Event_Time > lag(Departure_Time),
                                            Event_Time - lag(Departure_Time),
                                            NA
                                           )
                                    ),
         TravelTime_Hr = TravelTime_Sec / 3600, # 3,600 seconds in 1 hour
         
         # accounting for potential negative or zero travel times
         SpeedAvg_Mph = ifelse(TravelTime_Hr > 0,
                               TravelDistance_Mi / TravelTime_Hr,
                               NA
                              ),
         
         Start_ID = lag(StopID_Clean),
         Start_Desc = lag(Stop_Desc),
         StartStop_ID = ifelse(is.na(Start_ID),
                               paste("NULL", StopID_Clean, sep = "--"),
                               paste(Start_ID, StopID_Clean, sep = "--")
                              )
        ) %>% 
  as.data.frame()


rm(AllDays_AddVars)
str(AllDays_BusDay)

# summary(AllDays_BusDay)

# View(tail(AllDays_BusDay, 50))

```


Inspecting for issues with StartStop_ID (where the value is either NA or contains NULL). They ONLY exist when BusDay_EventNum = 1 (which is by design). So everything looks OK.
```{r}

View(group_by(AllDays_BusDay,
              StartStop_ID
             ) %>% 
       summarise(
         Cnt = n()
       ) %>% 
       arrange(desc(Cnt)
              )
    )

View(filter(AllDays_BusDay,
            (is.na(StartStop_ID) |
              str_detect(StartStop_ID, "NULL")
            ) &
              BusDay_EventNum != 1
           )
    )

```


Stats (quantiles) overall for TravelDistance_Mi.
```{r}

Quantiles_dt <- AllDays_BusDay %>% 
  mutate(TD_Mi_q2 = quantile(x = TravelDistance_Mi, probs = 0.02, na.rm = TRUE),
         TD_Mi_q98 = quantile(x = TravelDistance_Mi, probs = 0.98, na.rm = TRUE),
         TT_Sec_q2 = quantile(x = TravelTime_Sec, probs = 0.02, na.rm = TRUE),
         TT_Sec_q98 = quantile(x = TravelTime_Sec, probs = 0.98, na.rm = TRUE),
         TT_Hr_q2 = quantile(x = TravelTime_Hr, probs = 0.02, na.rm = TRUE),
         TT_Hr_q98 = quantile(x = TravelTime_Hr, probs = 0.98, na.rm = TRUE)
        ) %>% 
  data.table()


Stats <- Quantiles_dt %>% 
  mutate(TD_Mi_Mean = mean(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_Mean_F = mean(TravelDistance_Mi[TD_Mi_q2 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_q98],
                             na.rm = TRUE
                            ),
         TD_Mi_Med = median(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_Med_F = median(TravelDistance_Mi[TD_Mi_q2 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_q98],
                              na.rm = TRUE
                             ),
         TD_Mi_Cnt = sum(!is.na(TravelDistance_Mi)
                        ),
         TD_Mi_Cnt_F = sum(!is.na(TravelDistance_Mi[TD_Mi_q2 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_q98]
                                 )
                          ),
            
         TT_Sec_Mean = mean(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_Mean_F = mean(TravelTime_Sec[TT_Sec_q2 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_q98],
                              na.rm = TRUE
                             ),
         TT_Sec_Med = median(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_Med_F = median(TravelTime_Sec[TT_Sec_q2 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_q98],
                               na.rm = TRUE
                              ),
         TT_Sec_Cnt = sum(!is.na(TravelTime_Sec)
                         ),
         TT_Sec_Cnt_F = sum(!is.na(TravelTime_Sec[TT_Sec_q2 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_q98]
                                   )
                           ),

         TT_Hr_Mean = mean(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_Mean_F = mean(TravelTime_Hr[TT_Hr_q2 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_q98],
                             na.rm = TRUE
                            ),
         TT_Hr_Med = median(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_Med_F = median(TravelTime_Hr[TT_Hr_q2 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_q98],
                              na.rm = TRUE
                             ),
         TT_Hr_Cnt = sum(!is.na(TravelTime_Hr)
                        ),
         TT_Hr_Cnt_F = sum(!is.na(TravelTime_Hr[TT_Hr_q2 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_q98]
                                 )
                          )
        ) %>% 
  data.frame()

rm(AllDays_BusDay)
rm(Quantiles_dt)
str(Stats)
# View(head(Stats, 50))

```


Stats for StartStop_ID.
```{r}

Quantiles_SS_dt <- group_by(Stats,
                            StartStop_ID
                           ) %>% 
  mutate(TD_Mi_SS_q5 = quantile(x = TravelDistance_Mi, probs = 0.05, na.rm = TRUE),
         TD_Mi_SS_q95 = quantile(x = TravelDistance_Mi, probs = 0.95, na.rm = TRUE),
         TT_Sec_SS_q5 = quantile(x = TravelTime_Sec, probs = 0.05, na.rm = TRUE),
         TT_Sec_SS_q95 = quantile(x = TravelTime_Sec, probs = 0.95, na.rm = TRUE),
         TT_Hr_SS_q5 = quantile(x = TravelTime_Hr, probs = 0.05, na.rm = TRUE),
         TT_Hr_SS_q95 = quantile(x = TravelTime_Hr, probs = 0.95, na.rm = TRUE)
        ) %>% 
  data.table()


Stats_StSt <- group_by(Quantiles_SS_dt,
                       StartStop_ID
                      ) %>% 
  mutate(TD_Mi_SS_Mean = mean(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_SS_Mean_F = mean(TravelDistance_Mi[TD_Mi_SS_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SS_q95],
                                na.rm = TRUE
                               ),
         TD_Mi_SS_Med = median(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_SS_Med_F = median(TravelDistance_Mi[TD_Mi_SS_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SS_q95],
                                 na.rm = TRUE
                                ),
         TD_Mi_SS_Cnt = sum(!is.na(TravelDistance_Mi)
                           ),
         TD_Mi_SS_Cnt_F = sum(!is.na(TravelDistance_Mi[TD_Mi_SS_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SS_q95]
                                    )
                             ),
            
         TT_Sec_SS_Mean = mean(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_SS_Mean_F = mean(TravelTime_Sec[TT_Sec_SS_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SS_q95],
                                 na.rm = TRUE
                                ),
         TT_Sec_SS_Med = median(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_SS_Med_F = median(TravelTime_Sec[TT_Sec_SS_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SS_q95],
                                  na.rm = TRUE
                                 ),
         TT_Sec_SS_Cnt = sum(!is.na(TravelTime_Sec)),
         TT_Sec_SS_Cnt_F = sum(!is.na(TravelTime_Sec[TT_Sec_SS_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SS_q95]
                                     )
                              ),

         TT_Hr_SS_Mean = mean(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_SS_Mean_F = mean(TravelTime_Hr[TT_Hr_SS_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SS_q95],
                                na.rm = TRUE
                               ),
         TT_Hr_SS_Med = median(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_SS_Med_F = median(TravelTime_Hr[TT_Hr_SS_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SS_q95],
                                 na.rm = TRUE
                                ),
         TT_Hr_SS_Cnt = sum(!is.na(TravelTime_Hr)),
         TT_Hr_SS_Cnt_F = sum(!is.na(TravelTime_Hr[TT_Hr_SS_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SS_q95]
                                    )
                             )
        ) %>% 
  data.frame()

rm(Stats)
rm(Quantiles_SS_dt)
str(Stats_StSt)
# View(head(Stats_StSt, 50))

```


Stats for StartStop_ID with Event_Time_HrGroup.
```{r}

Quantiles_SSHG_dt <- group_by(Stats_StSt,
                              StartStop_ID,
                              Event_Time_HrGroup
                             ) %>% 
  mutate(TD_Mi_SSHG_q5 = quantile(x = TravelDistance_Mi, probs = 0.05, na.rm = TRUE),
         TD_Mi_SSHG_q95 = quantile(x = TravelDistance_Mi, probs = 0.95, na.rm = TRUE),
         TT_Sec_SSHG_q5 = quantile(x = TravelTime_Sec, probs = 0.05, na.rm = TRUE),
         TT_Sec_SSHG_q95 = quantile(x = TravelTime_Sec, probs = 0.95, na.rm = TRUE),
         TT_Hr_SSHG_q5 = quantile(x = TravelTime_Hr, probs = 0.05, na.rm = TRUE),
         TT_Hr_SSHG_q95 = quantile(x = TravelTime_Hr, probs = 0.95, na.rm = TRUE)
        ) %>% 
  data.table()


Stats_StSt_HrGrp <- group_by(Quantiles_SSHG_dt,
                             StartStop_ID,
                             Event_Time_HrGroup
                            ) %>% 
  mutate(TD_Mi_SSHG_Mean = mean(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_SSHG_Mean_F = mean(TravelDistance_Mi[TD_Mi_SSHG_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SSHG_q95],
                                  na.rm = TRUE
                                 ),
         TD_Mi_SSHG_Med = median(TravelDistance_Mi, na.rm = TRUE),
         TD_Mi_SSHG_Med_F = median(TravelDistance_Mi[TD_Mi_SSHG_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SSHG_q95],
                                   na.rm = TRUE
                                  ),
         TD_Mi_SSHG_Cnt = sum(!is.na(TravelDistance_Mi)
                             ),
         TD_Mi_SSHG_Cnt_F = sum(!is.na(TravelDistance_Mi[TD_Mi_SSHG_q5 <= TravelDistance_Mi & TravelDistance_Mi <= TD_Mi_SSHG_q95]
                                      )
                               ),
            
         TT_Sec_SSHG_Mean = mean(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_SSHG_Mean_F = mean(TravelTime_Sec[TT_Sec_SSHG_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SSHG_q95],
                                   na.rm = TRUE
                                  ),
         TT_Sec_SSHG_Med = median(TravelTime_Sec, na.rm = TRUE),
         TT_Sec_SSHG_Med_F = median(TravelTime_Sec[TT_Sec_SSHG_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SSHG_q95],
                                    na.rm = TRUE
                                   ),
         TT_Sec_SSHG_Cnt = sum(!is.na(TravelTime_Sec)),
         TT_Sec_SSHG_Cnt_F = sum(!is.na(TravelTime_Sec[TT_Sec_SSHG_q5 <= TravelTime_Sec & TravelTime_Sec <= TT_Sec_SSHG_q95]
                                       )
                                ),

         TT_Hr_SSHG_Mean = mean(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_SSHG_Mean_F = mean(TravelTime_Hr[TT_Hr_SSHG_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SSHG_q95],
                                  na.rm = TRUE
                                 ),
         TT_Hr_SSHG_Med = median(TravelTime_Hr, na.rm = TRUE),
         TT_Hr_SSHG_Med_F = median(TravelTime_Hr[TT_Hr_SSHG_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SSHG_q95],
                                   na.rm = TRUE
                                  ),
         TT_Hr_SSHG_Cnt = sum(!is.na(TravelTime_Hr)),
         TT_Hr_SSHG_Cnt_F = sum(!is.na(TravelTime_Hr[TT_Hr_SSHG_q5 <= TravelTime_Hr & TravelTime_Hr <= TT_Hr_SSHG_q95]
                                      )
                               )
        ) %>% 
  data.frame()

rm(Stats_StSt)
rm(Quantiles_SSHG_dt)
str(Stats_StSt_HrGrp)
# View(head(Stats_StSt_HrGrp, 50))

```


Feature engineering.

Calculating a variable to know if the RouteAlt changed. Could be useful in helping identifying weirdness in calculated distances and speeds.
```{r}

# rm(Stats_StSt_HrGrp)

AllDays_DirChange <- Stats_StSt_HrGrp %>%  # AllDays_BusDayRoute %>% 
  mutate(RteChange = ifelse(Route == Route_Lag1,
                            "Same",
                            "Change"
                           ),
         RteChange2 = factor(ifelse(is.na(RteChange),
                                    "Change",
                                    RteChange
                                   )
                            ),
         DirChange = ifelse(RouteAlt == RouteAlt_Lag1,
                            "Same",
                            "Change"
                           ),
         DirChange2 = factor(ifelse(is.na(DirChange),
                                    "Change",
                                    DirChange
                                   )
                            )
        )

# rm(AllDays_BusDayRoute)
rm(Stats_StSt_HrGrp)
str(AllDays_DirChange)

View(filter(AllDays_DirChange,
            between(RowNum_OG, 2570060, 2570080)
           ) %>% 
       select(-matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
             )
    )

```


Re-ordering the variables to ease with comprehension.
```{r}

AllDays_NewOrder <-  select(AllDays_DirChange,
                            RowNum_OG,
                            UniqueLatLng,
                            group,
                            StartStop_ID,
                            BusDay_EventNum,
                            Bus_ID,
                            Route,
                            RteChange2,
                            RouteAlt,
                            # RouteAlt_Lag1,
                            DirChange2,
                            Route_Direction,
                            Stop_Sequence,
                            Start_ID,
                            Start_Desc,
                            # Stop_ID,
                            StopID_Clean,
                            StopID_Indicator,
                            Stop_Desc,
                            countryCode,
                            Stop_State,
                            Stop_County,
                            Stop_City,
                            Stop_Zip,
                            Event_Type,
                            Event_Description,
                            Event_Time_Yr,
                            Event_Time_Mth,
                            Event_Time_Date,
                            Event_Time_Day,
                            Event_Time_Hr,
                            Event_Time_HrGroup,
                            Event_Time_Min,
                            Event_Time,
                            Departure_Time,
                            Dwell_Time,
                            Dwell_Time2,
                            Delta_Time,
                            Latitude,
                            Longitude,
                            Heading,
                            Odometer_Distance,
                            Odometer_Distance_Lag1,
                            Odometer_Distance_Mi,
                            TravelDistance_Ft,
                            TravelDistance_Mi,
                            TravelDistance_Mi_Hvrs,
                            TD_Mi_q2,
                            TD_Mi_q98,
                            TD_Mi_SS_q5,
                            TD_Mi_SS_q95,
                            TD_Mi_SSHG_q5,
                            TD_Mi_SSHG_q95,
                            TD_Mi_Mean,
                            TD_Mi_Mean_F,
                            TD_Mi_SS_Mean,
                            TD_Mi_SS_Mean_F,
                            TD_Mi_SSHG_Mean,
                            TD_Mi_SSHG_Mean_F,
                            TD_Mi_Med,
                            TD_Mi_Med_F,
                            TD_Mi_SS_Med,
                            TD_Mi_SS_Med_F,
                            TD_Mi_SSHG_Med,
                            TD_Mi_SSHG_Med_F,
                            TD_Mi_Cnt,
                            TD_Mi_Cnt_F,
                            TD_Mi_SS_Cnt,
                            TD_Mi_SS_Cnt_F,
                            TD_Mi_SSHG_Cnt,
                            TD_Mi_SSHG_Cnt_F,
                            TravelTime_Sec,
                            TT_Sec_q2,
                            TT_Sec_q98,
                            TT_Sec_SS_q5,
                            TT_Sec_SS_q95,
                            TT_Sec_SSHG_q5,
                            TT_Sec_SSHG_q95,
                            TT_Sec_Mean,
                            TT_Sec_Mean_F,
                            TT_Sec_SS_Mean,
                            TT_Sec_SS_Mean_F,
                            TT_Sec_SSHG_Mean,
                            TT_Sec_SSHG_Mean_F,
                            TT_Sec_Med,
                            TT_Sec_Med_F,
                            TT_Sec_SS_Med,
                            TT_Sec_SS_Med_F,
                            TT_Sec_SSHG_Med,
                            TT_Sec_SSHG_Med_F,
                            TT_Sec_Cnt,
                            TT_Sec_Cnt_F,
                            TT_Sec_SS_Cnt,
                            TT_Sec_SS_Cnt_F,
                            TT_Sec_SSHG_Cnt,
                            TT_Sec_SSHG_Cnt_F,
                            TravelTime_Hr,
                            TT_Hr_q2,
                            TT_Hr_q98,
                            TT_Hr_SS_q5,
                            TT_Hr_SS_q95,
                            TT_Hr_SSHG_q5,
                            TT_Hr_SSHG_q95,
                            TT_Hr_Mean,
                            TT_Hr_Mean_F,
                            TT_Hr_SS_Mean,
                            TT_Hr_SS_Mean_F,
                            TT_Hr_SSHG_Mean,
                            TT_Hr_SSHG_Mean_F,
                            TT_Hr_Med,
                            TT_Hr_Med_F,
                            TT_Hr_SS_Med,
                            TT_Hr_SS_Med_F,
                            TT_Hr_SSHG_Med,
                            TT_Hr_SSHG_Med_F,
                            TT_Hr_Cnt,
                            TT_Hr_Cnt_F,
                            TT_Hr_SS_Cnt,
                            TT_Hr_SS_Cnt_F,
                            TT_Hr_SSHG_Cnt,
                            TT_Hr_SSHG_Cnt_F,
                            SpeedAvg_Mph
                           )

rm(AllDays_DirChange)
str(select(AllDays_NewOrder,
           -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )
str(AllDays_NewOrder)

# View(head(AllDays_NewOrder, 500))
# View(tail(AllDays_NewOrder, 500))

```


Summarizing the data to help spot anomolies.
```{r}

# View(
  group_by(AllDays_NewOrder,
              Stop_City) %>% 
       summarise(Cnt_Num = n(),
                 Cnt_Pct = 100*Cnt_Num / (nrow(AllDays_NewOrder)
                                         )
                ) %>% 
       arrange(desc(Cnt_Num))
# )

summary(AllDays_NewOrder)

```


Investigation of TravelDistance_Mi.

View(TravDistMi_Pctiles): 99% of TravelDistance_Mi are about 1 mile or less...but some weird TravelDistance_Mi values (e.g., 584 miles traveled) exist.
```{r}

TravDistMi_Ntile <- as.data.frame(AllDays_NewOrder$TravelDistance_Mi) %>% 
  mutate(#Pctile = ntile(AllDays_NewOrder$TravelDistance_Mi, 100),
         #MinR = min_rank(AllDays_NewOrder$TravelDistance_Mi),
         PctR = percent_rank(AllDays_NewOrder$TravelDistance_Mi),
         PctR_Round = round(PctR, 2)
        ) 

colnames(TravDistMi_Ntile)[1] <- "TravelDistance_Mi"
# str(TravDistMi_Ntile)

TravDistMi_Ntile_Rows <- nrow(TravDistMi_Ntile)

# View(tail(TravDistMi_Ntile, 500))


TravDistMi_Pctiles <- group_by(TravDistMi_Ntile,
                               PctR_Round
                              ) %>% 
  summarise(
    MinTravDistMiAtPctile = min(TravelDistance_Mi),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / TravDistMi_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

rm(TravDistMi_Ntile)
rm(TravDistMi_Ntile_Rows)

View(TravDistMi_Pctiles)
TravDistMi_Pctiles

```


Investigation of TravelDistance_Mi.

Why are some TravelDistance_Mi "NA"? It looks like partially because the records are the first trip of the day (for that bus), so I purposefully set the distance to "NA". Another reason is due to the odometer recording a value less than the previous odometer recording. In most cases, I have no explanation for this - though I have observed about 67% of all instances where TravelDistance_Mi is NA (other than because it's the first record of the day) are instances where DirChange2 is "Change". This is weird and should be asked to WMATA.
```{r}

# View(head(AllDays_NewOrder, 500))

View(filter(AllDays_NewOrder,
            BusDay_EventNum != 1 # When BusDay_EventNum == 1, TravelDistance_Mi is NA by design (don't want to calculate distance based on yesterday's position)
           ) %>% 
       group_by(StartStop_ID) %>% 
       summarise(Cnts = sum(is.na(TravelDistance_Mi)
                           )
                ) %>% 
       arrange(desc(Cnts)
              )
    )

View(filter(AllDays_NewOrder,
            StartStop_ID == "1000245--1000211"
           ) %>% 
       select(RowNum_OG,
              StartStop_ID,
              Event_Time,
              Event_Time_HrGroup,
              Bus_ID,
              TravelDistance_Mi,
              TravelDistance_Mi_Hvrs,
              TD_Mi_SS_Mean,
              TD_Mi_SS_Mean_F,
              TD_Mi_SSHG_Mean,
              TD_Mi_SSHG_Mean_F,
              TD_Mi_SS_Med,
              TD_Mi_SS_Med_F,
              TD_Mi_SSHG_Med,
              TD_Mi_SSHG_Med_F,
              TD_Mi_SS_Cnt,
              TD_Mi_SS_Cnt_F,
              TD_Mi_SSHG_Cnt,
              TD_Mi_SSHG_Cnt_F
              ) %>% 
       mutate(Ratio_MeanToHvrs = TD_Mi_SS_Mean / TravelDistance_Mi_Hvrs) %>% 
       arrange(Event_Time)
    )

View(filter(AllDays_NewOrder,
            is.na(TravelDistance_Mi)
           )
    )

# These records are NA becuase the record is the first record of the day (the Event_Time_Date)
View(filter(AllDays_NewOrder,
            between(RowNum_OG, 326, 346) | # 336
              between(RowNum_OG, 591, 611) | # 601
              between(RowNum_OG, 845, 865) # 855
           )
    )

```


Investigation of TravelDistance_Mi.

These records are NA becuase the current record odometer is less than the previous record odometer. Theoretically, this should NOT happen. Me: it appears that about 67% of all instances where TravelDistance_Mi is NA (other than because it's the first record of the day) are instances where DirChange2 is "Change". This is weird and should be asked to WMATA.
```{r}

View(filter(AllDays_NewOrder,
            between(RowNum_OG, 194, 214) | # 204
              between(RowNum_OG, 440, 460) | # 450
              between(RowNum_OG, 478, 498) | # 488
              between(RowNum_OG, 510, 530) # 520
           )
    )

TestTable <- filter(AllDays_NewOrder,
                    BusDay_EventNum != 1
                   ) %>% 
  mutate(TravelDistance_NA = as.factor(ifelse(is.na(TravelDistance_Mi),
                                              "True",
                                              "False"
                                             )
                                      )
        ) %>%
  group_by(DirChange2, TravelDistance_NA) %>%
  summarise(TravDistMi_NACnts = n()
           )

# TestTable

TestTable_Spread <- as.data.frame(spread(TestTable,
                                         TravelDistance_NA,
                                         TravDistMi_NACnts
                                        )
                                 ) %>% 
  select(False,
         True
        )

row.names(TestTable_Spread) <- c("Change", "Same")
# str(TestTable_Spread)
# TestTable_Spread

prop.table(as.table(as.matrix(TestTable_Spread)
                   ),
           1
          )

prop.table(as.table(as.matrix(TestTable_Spread)
                   ),
           2
          )

```


Investigation of TravelDistance_Mi.

Let's look at just the TravelDistance_Mi values that are NOT "NA".
```{r}

rm(TestTable, TestTable_Spread)

TravelDistance_Mi_NoNA <- filter(AllDays_NewOrder,
                                 # TravelDistance_Mi != 0 &
                                 !is.na(TravelDistance_Mi)
                                )

dim(AllDays_NewOrder)
dim(TravelDistance_Mi_NoNA)
nrow(AllDays_NewOrder) - nrow(TravelDistance_Mi_NoNA)

str(TravelDistance_Mi_NoNA)
summary(TravelDistance_Mi_NoNA)

```


Investigation of TravelDistance_Mi.

Let's plot just the TravelDistance_Mi values that are NOT "NA".
```{r}

TravDistMi_HistDen <- ggplot(select(TravelDistance_Mi_NoNA,
                                    TravelDistance_Mi
                                   ),
                             aes(x = TravelDistance_Mi,
                                 y = ..density..
                                )
                            ) +
  geom_histogram(binwidth = 0.05, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  coord_cartesian(xlim = c(0, 1.5), ylim = c(0, 4.0)
                 ) +
  labs(title = "Variation in Distance Between Stops",
       x = "Travel Distance (miles)",
       y = "Density"
      )

TravDistMi_HistDen

```

Investigation of TravelDistance_Mi.

Looking at the extremely large TravelDistance_Mi values. Some (aprox 27%) of TravelDistance_Mi values > 1 mile are when the DirChange2 changes...but what about the other ~73%?
```{r}

rm(TravelDistance_Mi_NoNA)

# examples of weirdly large TravelDistance_Mi
View(filter(AllDays_NewOrder,
            TravelDistance_Mi > 1.1587121212 # 1.1587121212 is the 99th percentile
           ) %>% 
       arrange(desc(TravelDistance_Mi)
              )
    )


# Why are these extremes?  Airports?  Bus collection points?
View(filter(AllDays_NewOrder,
              between(RowNum_OG, 494044, 494064) | # 494054
              between(RowNum_OG, 494273, 494293) | # 494283
              between(RowNum_OG, 494626, 494646) | # 494636
              between(RowNum_OG, 1610156, 1610176) | # 1610166
              between(RowNum_OG, 2073074, 2073094) # 2073084
           )
    )

# Before Removing Runs
# View(filter(AllDays_Sorted,
#             between(RowNum_OG, 494044, 494064) | # 494054
#               between(RowNum_OG, 494273, 494293) | # 494283
#               between(RowNum_OG, 494626, 494646) | # 494636
#               between(RowNum_OG, 1610156, 1610176) | # 1610166
#               between(RowNum_OG, 2073074, 2073094) # 2073084
#            )
#     )

# After Removing Runs
# View(filter(AllDays_FirstStopID,
#             between(RowNum_OG, 494044, 494064) | # 494054
#               between(RowNum_OG, 494273, 494293) | # 494283
#               between(RowNum_OG, 494626, 494646) | # 494636
#               between(RowNum_OG, 1610156, 1610176) | # 1610166
#               between(RowNum_OG, 2073074, 2073094) # 2073084
#            )
#     )

```


Investigation of TravelDistance_Mi.

Any relation with DirChange2?  Doesn't look as if this is so.
```{r}

ExtremeTravDist <- filter(AllDays_NewOrder,
                          !is.na(TravelDistance_Mi)
                         ) %>% 
  mutate(TravDist_Extreme = ifelse(TravelDistance_Mi > 1.1587121212, # 1.1587121212 is the 99th percentile
                                   "True",
                                   "False"
                                  )
                          ) %>% 
  group_by(DirChange2, TravDist_Extreme) %>% 
  summarise(TravDistMI_ExtCnts = n()
           )

# ExtremeTravDist


ExtremeTravDist_Spread <- as.data.frame(spread(ExtremeTravDist,
                                               TravDist_Extreme,
                                               TravDistMI_ExtCnts
                                              )
                                       ) %>% 
  select(False,
         True
        )

row.names(ExtremeTravDist_Spread) <- c("Change", "Same")
# str(ExtremeTravDist_Spread)
# ExtremeTravDist_Spread

prop.table(as.table(as.matrix(ExtremeTravDist_Spread)
                   ),
           1
          )

prop.table(as.table(as.matrix(ExtremeTravDist_Spread)
                   ),
           2
          )

```


Investigation of TravelDistance_Mi.

Looking at specific buses and StartStop_ID.
```{r}

rm(ExtremeTravDist, ExtremeTravDist_Spread)

View(arrange(group_by(AllDays_NewOrder,
                      Bus_ID
                     ) %>% 
               summarise(DistTrav_Mean = mean(TravelDistance_Mi, na.rm = TRUE),
                         DistTrav_Med = median(TravelDistance_Mi, na.rm = TRUE)
                        ),
             desc(DistTrav_Med)
            )
    )


# example of extremely small TravelDistance_Mi values (looks like the odometer wasn't functioning)
View(filter(AllDays_NewOrder,
            Bus_ID == 6111 |
              Bus_ID == 7201 |
              Bus_ID == 8058
           ) %>% 
       arrange(Bus_ID, Event_Time)
    )


View(arrange(group_by(AllDays_NewOrder,
                      StartStop_ID
                     ) %>% 
               summarise(DistTrav_Mean = mean(TravelDistance_Mi, na.rm = TRUE),
                         DistTrav_Med = median(TravelDistance_Mi, na.rm = TRUE)
                        ),
             desc(DistTrav_Med)
            )
    )

# example of extremely large TravelDistance_Mi values...no idea why...
View(filter(AllDays_NewOrder,
            StartStop_ID == "1003665--12" |
              StartStop_ID == "1003665--5001925" |
              StartStop_ID == "3001038--3002565"
           ) %>% 
       arrange(StartStop_ID, Event_Time)
    )

```


Investigation of TravelDistance_Mi & TravelDistance_Mi_New.

If TravelDisntace_Mi is below the 5th percentile for that StartStop_ID, or if TravelDisntace_Mi is above the 95th percentile for that StartStop_ID, or if TravelDistance_Mi is NA (when the BusDay_EventNum !=1), consider this an outlier.  In this case, replace the value with the mean for that StartStop_ID and HourGroup (TD_Mi_SSHG_Mean_F), or if there are not enough values at the HourGroup level, replace it with the mean for that StartStop_ID.
```{r}

# View(tail(AllDays_NewOrder, 500))

AllDays_NewTravelDist <- 
  mutate(AllDays_NewOrder,
         TravelDistance_Mi_New = ifelse(!is.na(TravelDistance_Mi) & 
                                          (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                                             TravelDistance_Mi > TD_Mi_SSHG_q95
                                          ) &
                                          TD_Mi_SSHG_Cnt_F >= 20,
                                        TD_Mi_SSHG_Mean_F,
                                 ifelse(!is.na(TravelDistance_Mi) & 
                                          (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                                             TravelDistance_Mi > TD_Mi_SSHG_q95
                                          ) &
                                          TD_Mi_SSHG_Cnt_F < 20 &
                                          TD_Mi_SS_Cnt_F >= 20,
                                        TD_Mi_SS_Mean_F,
                                 ifelse(!is.na(TravelDistance_Mi) & 
                                          (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                                             TravelDistance_Mi > TD_Mi_SSHG_q95
                                          ) &
                                          TD_Mi_SS_Cnt_F < 20 &
                                          TD_Mi_SS_Cnt >= 20,
                                        TD_Mi_SS_Mean,
                                 ifelse(is.na(TravelDistance_Mi) &
                                          BusDay_EventNum != 1 &
                                          TravelDistance_Mi_Hvrs != 0,
                                        TravelDistance_Mi_Hvrs,
                                 ifelse(is.na(TravelDistance_Mi) &
                                          BusDay_EventNum != 1 &
                                          TravelDistance_Mi_Hvrs == 0,
                                        TD_Mi_SS_Mean,
                                        TravelDistance_Mi
                                       ))))),
         TravelDistance_Mi_New_Label = 
           factor(ifelse(!is.na(TravelDistance_Mi) &
                           (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                              TravelDistance_Mi > TD_Mi_SSHG_q95
                           ) &
                           TD_Mi_SSHG_Cnt_F >= 20,
                         "TD_Mi_SSHG_Mean_F",
                  ifelse(!is.na(TravelDistance_Mi) &
                           (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                              TravelDistance_Mi > TD_Mi_SSHG_q95
                           ) &
                           TD_Mi_SSHG_Cnt_F < 20 &
                           TD_Mi_SS_Cnt_F >= 20,
                         "TD_Mi_SS_Mean_F",
                  ifelse(!is.na(TravelDistance_Mi) &
                           (TravelDistance_Mi < TD_Mi_SSHG_q5 |
                              TravelDistance_Mi > TD_Mi_SSHG_q95
                           ) &
                           TD_Mi_SS_Cnt_F < 20 &
                           TD_Mi_SS_Cnt >= 20,
                         "TD_Mi_SS_Mean",
                  ifelse(is.na(TravelDistance_Mi) &
                           BusDay_EventNum != 1 &
                           TravelDistance_Mi_Hvrs != 0,
                         "TravelDistance_Mi_Hvrs",
                  ifelse(is.na(TravelDistance_Mi) &
                           BusDay_EventNum != 1 &
                           TravelDistance_Mi_Hvrs == 0,
                         "TD_Mi_SS_Mean",
                         "TravelDistance_Mi"
                        )))))
                 ),
         TravelDistance_Mi_NewHvrs = ifelse(!is.na(TravelDistance_Mi_Hvrs) &
                                              TravelDistance_Mi_Hvrs != 0 &
                                              (TravelDistance_Mi_New < TD_Mi_q2 |
                                                 TravelDistance_Mi_New > TD_Mi_q98
                                              ),
                                            TravelDistance_Mi_Hvrs,
                                            TravelDistance_Mi_New
                                           ),
         TravelDistance_Mi_NewHvrs_Label =
           factor(ifelse(!is.na(TravelDistance_Mi_Hvrs) &
                           TravelDistance_Mi_Hvrs != 0 &
                           (TravelDistance_Mi_New < TD_Mi_q2 |
                              TravelDistance_Mi_New > TD_Mi_q98
                           ),
                         "TravelDistance_Mi_Hvrs",
                         as.character(TravelDistance_Mi_New_Label)
                        )
                 ),
         SpeedAvg_Mph_NewHvrs = TravelDistance_Mi_NewHvrs / TravelTime_Hr
        )

rm(AllDays_NewOrder)
str(AllDays_NewTravelDist)

```


Investigation of TravelDistance_Mi & TravelDistance_Mi_Hvrs & TravelDistance_Mi_New.

Quick summary and then correlation calculation.
```{r}

# 38 rows meet this criteria anymore  --  appears to be the case when both the Lat Long calculations, and the TravelDistance calculations did not function properly.
View(filter(AllDays_NewTravelDist,
            is.na(TravelDistance_Mi_New) &
              BusDay_EventNum != 1
           )
    )

View(AllDays_NewTravelDist %>% 
       arrange(desc(TravelDistance_Mi_New)) %>% 
       head(500)
    )

message("All records")
summary(select(AllDays_NewTravelDist,
               TravelDistance_Mi,
               TravelDistance_Mi_Hvrs,
               TravelDistance_Mi_New,
               TravelDistance_Mi_NewHvrs
              )
       )

message("BusDay_EventNum != 1")
summary(select(filter(AllDays_NewTravelDist,
                      BusDay_EventNum != 1
                     ),
               TravelDistance_Mi,
               TravelDistance_Mi_Hvrs,
               TravelDistance_Mi_New,
               TravelDistance_Mi_NewHvrs
              )
       )

message("All records")
cor(select(AllDays_NewTravelDist,
           TravelDistance_Mi,
           TravelDistance_Mi_Hvrs,
           TravelDistance_Mi_New,
           TravelDistance_Mi_NewHvrs
          ),
    use = "pairwise.complete.obs"
  )

```


Investigation of TravelDistance_Mi_NewHvrs_Label & TravelDistance_Mi_NewHvrs_Label.

Show how the labels changed.
```{r}

group_by(AllDays_NewTravelDist,
         TravelDistance_Mi_New_Label,
         TravelDistance_Mi_NewHvrs_Label
        ) %>% 
  summarise(CntNum = n(),
            CntPct = format(CntNum / nrow(AllDays_NewTravelDist),
                            scientific = 9999
                           )
           ) %>% 
  arrange(desc(CntPct)
         )

```


Investigation of TravelDistance_Mi & TravelDistance_Mi_Hvrs & TravelDistance_Mi_New.

Graphing the two methods of calculating TravelDistance_Mi.

First, let's get create a function to plot the liner model equation.
```{r}

lm_eqn <- function(df, y, x){
  m <- lm(y ~ x, df)
  
  l <- list(a = format(coef(m)[1], digits = 2),
            b = format(abs(coef(m)[2]), digits = 2),
            s1 = ifelse(test = coef(m)[2] > 0,
                        yes = "+",
                        no = "-"
                       ),
            r2 = format(summary(m)$r.squared,
                        digits = 3
                       )
           )
  
  eq <- substitute(italic(y) == a~~s1~~b %.% italic(x)*","~~italic(r)^2~"="~r2,
                   l
                  )
  
  as.character(as.expression(eq)
              )             
}

```


Investigation of TravelDistance_Mi & TravelDistance_Mi_NewHvrs.

Scatter plot (using a 10% sample to making plotting time faster and to reduce un-needed data in the "same" splot).
```{r}

set.seed(123456789)
AllDays_NewTravelDist_10Pct <- filter(AllDays_NewTravelDist,
                                      !is.na(TravelDistance_Mi_NewHvrs) &
                                        !is.na(TravelDistance_Mi)
                                     ) %>% 
  rename(DistMethod = TravelDistance_Mi_NewHvrs_Label) %>% 
  sample_frac(0.1)


TravDist_MiVsCalc <- ggplot(select(AllDays_NewTravelDist_10Pct,
                                   TravelDistance_Mi_NewHvrs,
                                   TravelDistance_Mi,
                                   DistMethod
                                  ),
                            aes(x = TravelDistance_Mi,
                                y = TravelDistance_Mi_NewHvrs,
                                colour = DistMethod
                               )
                           ) +
  scale_colour_manual(values = c("red","blue", "green", "orange", "black")
                     ) +
  geom_point(shape = 1, alpha = 0.5) +
  scale_shape(solid = FALSE) +
  geom_smooth(method = "lm", colour = "blue") +
  geom_abline(intercept = 0, slope = 1, colour = "red") +
  coord_cartesian(xlim = c(0, 1.5), ylim = c(0, 1.5)
                 ) +
  scale_x_continuous(breaks = seq(0, 1.5, 0.25)
                    ) +
  scale_y_continuous(breaks = seq(0, 1.5, 0.25)
                    ) +
  theme(legend.position = "bottom", #c(0.85, 0.40),
        legend.text = element_text(size = 6)
       ) +
  annotate(label = lm_eqn(df = AllDays_NewTravelDist_10Pct,
                          x = AllDays_NewTravelDist_10Pct$TravelDistance_Mi,
                          y = AllDays_NewTravelDist_10Pct$TravelDistance_Mi_NewHvrs
                         ),
           # x = 62,
           # y = 20,
           x = 0.70,
           y = 0.00,
           geom = "text",
           size = 3,
           colour = "blue",
           parse = TRUE
          ) +
  annotate(label = "Reference Line (slope = 1)",
           # x = 16,
           # y = 30,
           x = 0.80,
           y = 1.05,
           geom = "text",
           size = 3,
           colour = "red"
          ) +
  labs(title = "TravelDistance_Mi vs. TravelDistance_Mi_NewHvrs",
       x = "TravelDistance_Mi",
       y = "TravelDistance_Mi_NewHvrs"
      )
# +
#   geom_jitter()

TravDist_MiVsCalc

```


Investigation of TravelDistance_Mi & TravelDistance_Mi_Hvrs & TravelDistance_Mi_New.

Graphing test with rbokeh.
```{r, eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}

TravDist_MiVsCalc_Bokeh <- figure(data = select(AllDays_NewTravelDist_10Pct,
                                                TravelDistance_Mi_NewHvrs,
                                                TravelDistance_Mi,
                                                DistMethod
                                               ),
                                  xlim = c(0, 1.5),
                                  ylim = c(0, 1.5),
                                  legend_location = "bottom_right"
                                 ) %>% 
  ly_points(x = TravelDistance_Mi,
            y = TravelDistance_Mi_NewHvrs,
            color = DistMethod,
            hover = c(TravelDistance_Mi_NewHvrs, TravelDistance_Mi, DistMethod)
           ) %>% 
  ly_abline(a = 0, b = 1, color = "red")

TravDist_MiVsCalc_Bokeh

```


Investigation of TravelDistance_Mi_New.

Calculating the minimum TravelDistance_Mi_New value at each percentile.
```{r}

rm(TravDist_MiVsCalc_Bokeh)
rm(AllDays_NewTravelDist_10Pct)


summary(select(AllDays_NewTravelDist,
               TravelDistance_Mi,
               TravelDistance_Mi_Hvrs,
               TravelDistance_Mi_New,
               TravelDistance_Mi_NewHvrs
              )
       )

summary(select(filter(AllDays_NewTravelDist,
                      BusDay_EventNum != 1
                     ),
               TravelDistance_Mi,
               TravelDistance_Mi_Hvrs,
               TravelDistance_Mi_New,
               TravelDistance_Mi_NewHvrs
              )
       )


TravDistMiN_Ntile <- as.data.frame(select(AllDays_NewTravelDist,
                                          StartStop_ID,
                                          TravelDistance_Mi_New_Label,
                                          # TravelDistance_Mi_NewHvrs_Label,
                                          TravelDistance_Mi_New
                                          # TravelDistance_Mi_NewHvrs
                                         )
                                  ) %>% 
  mutate(PctR_N = percent_rank(AllDays_NewTravelDist$TravelDistance_Mi_New),
         # PctR_H = percent_rank(AllDays_NewTravelDist$TravelDistance_Mi_NewHvrs),
         PctR_Round_N = round(PctR_N, 2)
         # PctR_Round_H = round(PctR_H, 2)
        ) 

# str(TravDistMiN_Ntile)
# View(head(TravDistMiN_Ntile, 500))

TravDistMiN_Ntile_Rows <- nrow(TravDistMiN_Ntile)

# View(tail(TravDistMiN_Ntile, 500))


TravDistMiN_Pctiles <- group_by(TravDistMiN_Ntile,
                                PctR_Round_N
                               ) %>% 
  summarise(
    MinTDMiAtPctile_N = min(TravelDistance_Mi_New),
    # MinTDMiAtPctile_H = min(TravelDistance_Mi_NewHvrs),
    CntsAtPctile_N = sum(!is.na(TravelDistance_Mi_New)),
    # CntsAtPctile_H = sum(!is.na(TravelDistance_Mi_NewHvrs)),
    PctsAtPctile_N = CntsAtPctile_N / TravDistMiN_Ntile_Rows
    # PctsAtPctile_H = CntsAtPctile_H / TravDistMiN_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP_N = cumsum(PctsAtPctile_N)
         # CumSumPAtP_H = cumsum(PctsAtPctile_H)
        )

# View(TravDistMiN_Pctiles)
TravDistMiN_Pctiles

```


Investigation of TravelDistance_Mi_NewHvrs

Calculating the minimum TravelDistance_Mi_NewHvrs value at each percentile.
```{r}

TravDistMiH_Ntile <- as.data.frame(select(AllDays_NewTravelDist,
                                          StartStop_ID,
                                          # TravelDistance_Mi_New_Label,
                                          TravelDistance_Mi_NewHvrs_Label,
                                          # TravelDistance_Mi_New,
                                          TravelDistance_Mi_NewHvrs
                                         )
                                  ) %>% 
  mutate(# PctR_N = percent_rank(AllDays_NewTravelDist$TravelDistance_Mi_New),
         PctR_H = percent_rank(AllDays_NewTravelDist$TravelDistance_Mi_NewHvrs),
         # PctR_Round_N = round(PctR_N, 2),
         PctR_Round_H = round(PctR_H, 2)
        ) 

# str(TravDistMiH_Ntile)
# View(head(TravDistMiH_Ntile, 500))

TravDistMiH_Ntile_Rows <- nrow(TravDistMiH_Ntile)

# View(tail(TravDistMiH_Ntile, 500))


TravDistMiH_Pctiles <- group_by(TravDistMiH_Ntile,
                                PctR_Round_H
                               ) %>% 
  summarise(
    # MinTDMiAtPctile_N = min(TravelDistance_Mi_New),
    MinTDMiAtPctile_H = min(TravelDistance_Mi_NewHvrs),
    # CntsAtPctile_N = sum(!is.na(TravelDistance_Mi_New)),
    CntsAtPctile_H = sum(!is.na(TravelDistance_Mi_NewHvrs)),
    # PctsAtPctile_N = CntsAtPctile_N / TravDistMiH_Ntile_Rows,
    PctsAtPctile_H = CntsAtPctile_H / TravDistMiH_Ntile_Rows
  ) %>% 
  mutate(# CumSumPAtP_N = cumsum(PctsAtPctile_N),
         CumSumPAtP_H = cumsum(PctsAtPctile_H)
        )

# View(TravDistMiH_Pctiles)
TravDistMiH_Pctiles

```


Join TravDistMiH_Pctiles, TravDistMiN_Pctiles, and TravDistMi_Pctiles.

~11% of rides are still showing as less than 0.1 miles of TravelDistance_Mi_NewHvrs.
```{r}

rm(TravDistMiN_Ntile_Rows, TravDistMiH_Ntile_Rows, TravDistMiN_Ntile, TravDistMiH_Ntile)


# View(TravDistMi_Pctiles)
# View(TravDistMiN_Pctiles)
# View(TravDistMiH_Pctiles)

TravDistMi_Pctiles_All <- inner_join(x = TravDistMi_Pctiles,
                                     y = TravDistMiN_Pctiles,
                                     by = c("PctR_Round" = "PctR_Round_N")
                                    ) %>% 
  inner_join(y = TravDistMiH_Pctiles,
             by = c("PctR_Round" = "PctR_Round_H")
            ) %>% 
  select(PctR_Round,
         MinTravDistMiAtPctile,
         MinTDMiAtPctile_N,
         MinTDMiAtPctile_H,
         CntsAtPctile,
         CntsAtPctile_N,
         CntsAtPctile_H,
         PctsAtPctile,
         PctsAtPctile_N,
         PctsAtPctile_H,
         CumSumPAtP,
         CumSumPAtP_N,
         CumSumPAtP_H
         )

# str(TravDistMi_Pctiles_All)

rm(TravDistMi_Pctiles, TravDistMiN_Pctiles,TravDistMiH_Pctiles)


View(TravDistMi_Pctiles_All)
TravDistMi_Pctiles_All

```


Investigation of TravelDistance_Mi_New.

Why are there still some small or large TravelDistance_Mi_NewHvrs values.
```{r}

View(filter(AllDays_NewTravelDist,
            !is.na(TravelDistance_Mi_NewHvrs)
           ) %>% 
       select(-matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
             ) %>% 
       arrange(TravelDistance_Mi_NewHvrs) %>%
       head(500)
    )

# examples of the smallest TravelDistance_Mi_NewHvrs values.
View(filter(AllDays_NewTravelDist,
            (RowNum_OG >= 1424440 & RowNum_OG <= 1424460) | # 1424450  --  direction change
                (RowNum_OG >= 763292 & RowNum_OG <= 763312) | # 763302  --  direction change
                (RowNum_OG >= 1679093 & RowNum_OG <= 1679113) | # 1679103  --  direction change
                (RowNum_OG >= 2860918 & RowNum_OG <= 2860938) # 2860928  --  looks correct
           ) %>% 
       select(-matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
             )
    )


View(filter(AllDays_NewTravelDist,
            !is.na(TravelDistance_Mi_NewHvrs)
           ) %>% 
       select(-matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
             ) %>% 
       arrange(desc(TravelDistance_Mi_NewHvrs)
              ) %>%
       head(500)
    )

# examples of the largest TravelDistance_Mi_NewHvrs values.
View(filter(AllDays_NewTravelDist,
            (RowNum_OG >= 1092000 & RowNum_OG <= 1092050) | # 1092030  --  direction change
                (RowNum_OG >= 1609460 & RowNum_OG <= 1609480) | # 1609470  -- direction change 
                (RowNum_OG >= 508904 & RowNum_OG <= 508924) | # 508914  --  direction change & original StopID was bad
                (RowNum_OG >= 2476345 & RowNum_OG <= 2476365) # 2476355  --  direction change
           ) %>% 
       select(-matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
             )
    )

```


Investigation of TravelTime_Hr.

View(TravDistMi_Pctiles): 98% of TravelTime_Hr are between 7 seconds and 464 seconds (~8 minutes).
```{r}

TravTimeHr_Ntile <- select(AllDays_NewTravelDist,
                           TravelTime_Hr
                          ) %>% 
  mutate(# Pctile = ntile(AllDays_NewTravelDist$TravelTime_Hr, 100),
         # MinR = min_rank(AllDays_NewTravelDist$TravelTime_Hr),
         PctR = percent_rank(AllDays_NewTravelDist$TravelTime_Hr),
         PctR_Round = round(PctR, 2)
        ) 

# str(TravTimeHr_Ntile)

TravTimeHr_Ntile_Rows <- nrow(TravTimeHr_Ntile)

# View(tail(TravTimeHr_Ntile, 500))


TravTimeHr_Pctiles <- group_by(TravTimeHr_Ntile,
                               PctR_Round
                              ) %>% 
  summarise(
    MinTravTimeHrAtPctile = min(TravelTime_Hr),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / TravTimeHr_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile),
         MinTravTimeSecAtPctile = MinTravTimeHrAtPctile * 3600
        )

rm(TravTimeHr_Ntile_Rows)
rm(TravTimeHr_Ntile)
View(TravTimeHr_Pctiles)
TravTimeHr_Pctiles

```


Investigation of TravelTime_Hr.

Histogram of TravelTime_Sec.
```{r}

TravTime_Sec_HistDen <- ggplot(filter(select(AllDays_NewTravelDist,
                                             TravelTime_Sec
                                            ),
                                      !is.na(TravelTime_Sec)
                                     ),
                               aes(x = TravelTime_Sec,
                                   y = ..density..
                                  )
                          ) +
  geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  # stat_bin(binwidth = 5,
  #          geom = "text",
  #          size = 2.5,
  #          vjust = 1.5,
  #          aes(label = format(..count.., big.mark = ",")
  #             ),
  #         ) +
  coord_cartesian(xlim = c(0, 180), ylim = c(0, 0.02)
                 ) +
  #  theme(legend.position="none") +
  labs(title = "Variation in Travel Time",
       x = "Travel Time (sec)",
       y = "Density"
      )

TravTime_Sec_HistDen

```


Investigation of TravelTime_Sec.

TravelTime_Sec values are NA.
```{r}

summary(AllDays_NewTravelDist$TravelTime_Sec)


View(select(AllDays_NewTravelDist,
            -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
           ) %>% 
       filter(is.na(TravelTime_Sec) &
                BusDay_EventNum != 1  # TravelTime purposefully not calculated here
             )
    )

# examples of TravelTime_Sec values that are NA. These are NA because the Event_Time & Departure_Time readings are not accurate (i.e., the previous Departure_Time is BEFORE or EQUAL TO the current Event_Time).
View(filter(AllDays_NewTravelDist,
            (RowNum_OG >= 90809 & RowNum_OG <= 90829) | # 90819
                (RowNum_OG >= 90881 & RowNum_OG <= 90901) | # 90891
                (RowNum_OG >= 2597066 & RowNum_OG <= 2597086) | # 2597076
                (RowNum_OG >= 2613305 & RowNum_OG <= 2613325) # 2613315
           ) %>% 
       select(-matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt"))
    )

```


Investigation of TravelTime_Sec.

TravelTime_Sec values are extremely small.
```{r}

View(select(AllDays_NewTravelDist,
            -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
           ) %>% 
       filter(!is.na(TravelTime_Sec)
             ) %>% 
       arrange(TravelTime_Sec,
               desc(SpeedAvg_Mph_NewHvrs)
              ) %>%
       head(500)
    )

# examples where TravelTime_Sec is small (1 sec) and SpeedAvg_Mph_NewHvrs is large.
View(select(AllDays_NewTravelDist,
            -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
           ) %>% 
       filter((RowNum_OG >= 2217353 & RowNum_OG <= 2217373) | # 2217363
                (RowNum_OG >= 3090321 & RowNum_OG <= 3090341) | # 3090331
                (RowNum_OG >= 80764 & RowNum_OG <= 80784) | # 80774
                (RowNum_OG >= 33840 & RowNum_OG <= 33860) # 33850
           )
    )

```


Investigation of TravelTime_Sec.

TravelTime_Sec values are extremely large.
```{r}

View(select(AllDays_NewTravelDist,
            -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
           ) %>% 
       filter(!is.na(TravelTime_Sec)
             ) %>% 
       arrange(desc(TravelTime_Sec),
               SpeedAvg_Mph_NewHvrs
              ) %>%
       head(500)
    )

# examples where TravelTime_Sec is large and SpeedAvg_Mph_NewHvrs is small.
View(select(AllDays_NewTravelDist,
            -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
           ) %>% 
       filter((RowNum_OG >= 1007703 & RowNum_OG <= 1007723) | # 1007713
                (RowNum_OG >= 2373564 & RowNum_OG <= 2373584) | # 2373574
                (RowNum_OG >= 864379 & RowNum_OG <= 864399) | # 864389
                (RowNum_OG >= 2570060 & RowNum_OG <= 2570080) # 2570070
           )
    )

```


Investigation of TravelTime_Sec.

Are large TravelTime_Sec values related to RouteChanges? Looks likely. When the Bus involves a Route "change", there is almost twice as likely to be a case of an outlier TravelTime_Sec value (on the high side).
```{r}

TTLargeRteChng <- select(AllDays_NewTravelDist,
                         -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
                        ) %>% 
  mutate(TT_Out = factor(ifelse(TravelTime_Sec > 464,  # this is the 99th percentile
                                "Outlier",
                                "Normal"
                               )
                        )
        )

# str(TTLargeRteChng)


TTLargeRteChng_Cnts <- group_by(TTLargeRteChng,
                                RteChange2,
                                TT_Out
                               ) %>% 
  summarise(Cnts = n()
           )

TTLargeRteChng_Spread <- as.data.frame(spread(TTLargeRteChng_Cnts,
                                              TT_Out,
                                              Cnts
                                             )
                                      ) %>%
  select(-RteChange2)

row.names(TTLargeRteChng_Spread) <- c("Change", "Same")
# str(TTLargeRteChng_Spread)


# When the Bus involves a Route "change", there is almost twice as likely to be a case of an outlier TravelTime_Sec value.
TTLargeRteChng_Spread
prop.table(as.table(as.matrix(TTLargeRteChng_Spread)
                   ),
           1
          )

prop.table(as.table(as.matrix(TTLargeRteChng_Spread)
                   ),
           2
          )

# rm(TTLargeRteChng, TTLargeRteChng_Spread)
         
```


Investigation of TravelTime_Sec.

Are large TravelTime_Sec values related to RouteChanges? Looks likely.
```{r}

View(filter(TTLargeRteChng,
            !is.na(TravelTime_Sec) &
              RteChange2 == "Same"
           ) %>% 
       arrange(desc(TravelTime_Sec),
               SpeedAvg_Mph_NewHvrs
              ) %>%
       head(500)
    )


# examples where TravelTime_Sec is large and SpeedAvg_Mph_NewHvrs is small.
View(filter(TTLargeRteChng,
            (RowNum_OG >= 2250290 & RowNum_OG <= 2250310) | # 2250300
              (RowNum_OG >= 867717 & RowNum_OG <= 867737) | # 867727
              (RowNum_OG >= 864379 & RowNum_OG <= 864399) | # 864389
              (RowNum_OG >= 808395 & RowNum_OG <= 808415) # 808405
           )
    )


```


Investigation of TravelTime_Sec.

If TravelTime_Sec is below the 5th percentile for that StartStop_ID, or if TravelTime_Sec is above the 95th percentile for that StartStop_ID,  consider this an outlier.  In this case, replace the value with the mean for that StartStop_ID and HourGroup (TT_Sec_SSHG_Mean_F), or if there are not enough values at the HourGroup level, replace it with the mean for that StartStop_ID.
```{r}

rm(TTLargeRteChng, TTLargeRteChng_Cnts, TTLargeRteChng_Spread)


NewTravTime <- mutate(AllDays_NewTravelDist,
                      TT_Sec_New = ifelse(!is.na(TravelTime_Sec) &
                                            (TravelTime_Sec < TT_Sec_SSHG_q5 |
                                               TravelTime_Sec > TT_Sec_SSHG_q95
                                            ) &
                                            TT_Sec_SSHG_Cnt_F >= 20,
                                          TT_Sec_SSHG_Mean_F,
                                   ifelse(!is.na(TravelTime_Sec) &
                                            (TravelTime_Sec < TT_Sec_SSHG_q5 |
                                               TravelTime_Sec > TT_Sec_SSHG_q95
                                            ) &
                                            TT_Sec_SSHG_Cnt_F < 20 &
                                            TT_Sec_SS_Cnt_F >= 20,
                                          TT_Sec_SS_Mean_F,
                                   ifelse(!is.na(TravelTime_Sec) &
                                            (TravelTime_Sec < TT_Sec_SSHG_q5 |
                                               TravelTime_Sec > TT_Sec_SSHG_q95
                                            ) &
                                            TT_Sec_SS_Cnt_F < 20 &
                                            TT_Sec_SS_Cnt >= 20,
                                          TT_Sec_SS_Mean,
                                   ifelse(!is.na(TravelTime_Sec) &
                                            (TravelTime_Sec < TT_Sec_SSHG_q5 |
                                               TravelTime_Sec > TT_Sec_SSHG_q95
                                            ) &
                                            TT_Sec_SS_Cnt_F < 20 &
                                            TT_Sec_SS_Cnt < 20 &
                                            RteChange2 == "Change",
                                          NA,
                                          TravelTime_Sec
                                         )))),
                      
                      TT_Sec_New_Label = 
           factor(ifelse(!is.na(TravelTime_Sec) &
                           (TravelTime_Sec < TT_Sec_SSHG_q5 |
                              TravelTime_Sec > TT_Sec_SSHG_q95
                           ) &
                           TT_Sec_SSHG_Cnt_F >= 20,
                         "TT_Sec_SSHG_Mean_F",
                  ifelse(!is.na(TravelTime_Sec) &
                           (TravelTime_Sec < TT_Sec_SSHG_q5 |
                              TravelTime_Sec > TT_Sec_SSHG_q95
                           ) &
                           TT_Sec_SSHG_Cnt_F < 20 &
                           TT_Sec_SS_Cnt_F >= 20,
                         "TT_Sec_SS_Mean_F",
                  ifelse(!is.na(TravelTime_Sec) &
                           (TravelTime_Sec < TT_Sec_SSHG_q5 |
                              TravelTime_Sec > TT_Sec_SSHG_q95
                            ) &
                           TT_Sec_SS_Cnt_F < 20 &
                           TT_Sec_SS_Cnt >= 20,
                         "TT_Sec_SS_Mean",
                  ifelse(!is.na(TravelTime_Sec) &
                           (TravelTime_Sec < TT_Sec_SSHG_q5 |
                              TravelTime_Sec > TT_Sec_SSHG_q95
                           ) &
                           TT_Sec_SS_Cnt_F < 20 &
                           TT_Sec_SS_Cnt < 20 &
                           RteChange2 == "Change",
                         NA,
                         "TravelTime_Sec"
                        ))))
                 ),
                  
                  TT_Hr_New = TT_Sec_New / (60 * 60)
           )


dim(AllDays_NewTravelDist)
dim(NewTravTime)
rm(AllDays_NewTravelDist)

summary(select(NewTravTime,
           -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )

str(select(NewTravTime,
           TravelTime_Sec,
           TT_Sec_New,
           TT_Sec_New_Label,
           TT_Hr_New
          )
   )


summary(select(NewTravTime,
               TravelTime_Sec,
               TT_Sec_New,
               TT_Sec_New_Label,
               TT_Hr_New
              )
       )

```


Test investigation of just the X2 Route. Box plots for time between bus arrivals (by HourGroup).
```{r}

View(head(select(NewTravTime,
                 -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
                )
         )
    )

X2 <- select(NewTravTime,
             -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
            ) %>% 
  filter(Route == "X2")

str(X2)

View(head(arrange(X2,
                  Bus_ID,
                  Event_Time
                 ),
          500
         )
    )

X2_ByStop <- group_by(X2,
                      StopID_Clean
                     ) %>% 
  arrange(StopID_Clean,
          Event_Time) %>% 
  mutate(Event_Time_L1 = lag(Event_Time),
         TimeToEvent_Sec = as.numeric(Event_Time - Event_Time_L1),
         TimeToEvent_Min = TimeToEvent_Sec / 60
        )

View(head(X2_ByStop, 500))


# Count_Values is needed to display the medians on the box plots
Count_Values <- ddply(as.data.frame(X2_ByStop),
                      .(Event_Time_HrGroup),
                      summarise,
                      Value_Counts = median(TimeToEvent_Min, na.rm = TRUE)
                     )

TimeBtwEvents_X2_BoxPlot <- ggplot(select(as.data.frame(X2_ByStop),
                                          TimeToEvent_Min,
                                          Event_Time_HrGroup
                                         ),
                                   aes(factor(Event_Time_HrGroup),
                                       TimeToEvent_Min,
                                       fill = factor(Event_Time_HrGroup)
                                      )
                                  ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE, na.rm = TRUE) +
  geom_text(data = Count_Values,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 3,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 120)
                 ) +
  labs(title = "How Often an X2 Arrives at a Given Stop",
       x = "Hour Group",
       y = "Time Between Busses (min)"
      )

TimeBtwEvents_X2_BoxPlot

```


Test investigation of just the X2 Route. Violin plots for time between bus arrivals (by Hour Group).
```{r}

TimeBtwEvents_X2_ViolinPlot <- ggplot(select(as.data.frame(X2_ByStop),
                                             TimeToEvent_Min,
                                             Event_Time_HrGroup
                                             ),
                                      aes(factor(Event_Time_HrGroup),
                                          TimeToEvent_Min,
                                          fill = factor(Event_Time_HrGroup)
                                         )
                                     ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = Count_Values,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 80)
                 ) +
  labs(title = "How Often an X2 Arrives at a Given Stop",
       x = "Hour Group",
       y = "Time Between Busses (min)"
      )

TimeBtwEvents_X2_ViolinPlot

```


Test investigation of just the X2 Route. Box plots for time between bus arrivals (by Zip Code).
```{r}

# Count_Values is needed to display the medians on the box plots
Count_Values_z <- ddply(as.data.frame(X2_ByStop),
                        .(Stop_Zip),
                        summarise,
                        Value_Counts = median(TimeToEvent_Min, na.rm = TRUE)
                       )

TimeBtwEvents_X2_BoxPlot_z <- ggplot(select(as.data.frame(X2_ByStop),
                                            TimeToEvent_Min,
                                            Stop_Zip
                                           ),
                                     aes(factor(Stop_Zip),
                                         TimeToEvent_Min,
                                         fill = factor(Stop_Zip)
                                        )
                                    ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE, na.rm = TRUE) +
  geom_text(data = Count_Values_z,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 3,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 100)
                 ) +
  labs(title = "How Often an X2 Arrives at a Given Stop",
       x = "Zip Code of Destination",
       y = "Time Between Busses (min)"
      )

TimeBtwEvents_X2_BoxPlot_z

```


Test investigation of just the X2 Route. Violin plots for time between bus arrivals (by Zip Code).
```{r}

TimeBtwEvents_X2_ViolinPlot_z <- ggplot(select(as.data.frame(X2_ByStop),
                                               TimeToEvent_Min,
                                               Stop_Zip
                                               ),
                                        aes(factor(Stop_Zip),
                                            TimeToEvent_Min,
                                            fill = factor(Stop_Zip)
                                           )
                                       ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = Count_Values_z,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 60)
                 ) +
  labs(title = "How Often an X2 Arrives at a Given Stop",
       x = "Zip Code of Destination",
       y = "Time Between Busses (min)"
      )

TimeBtwEvents_X2_ViolinPlot_z

```


Waiting time analyses.

Munging and sampling data to go from time beteen buses to "average" waiting time.

First, get the max and min times of bus stops (each day, and for each route).
```{r}

rm(X2, X2_ByStop, X2_Long, X2_Pct)


RouteMinMax <- group_by(NewTravTime,
                        Route,
                        Event_Time_Date
                       ) %>% 
  summarise(MinTime = min(Event_Time),
            MaxTime = max(Event_Time)
           )

str(RouteMinMax)
View(RouteMinMax)
head(RouteMinMax, 50)

```


Waiting time analyses.

Munging and sampling data to go from time beteen buses to "average" waiting time.

(Pulls here are done by day, as the data are too large to do at once.)
```{r eval=FALSE}

# View(head(NewTravTime, 500))

# For each record, create a random datetime between the first and last stop for that bus route (on that day).
for(i in 3:7){

set.seed(123456789)
Samp <- select(NewTravTime,
               RowNum_OG,
               Route,
               # RouteGroup,
               Event_Time_Date,
               StopID_Clean,
               starts_with("Event")
              ) %>% 
  filter(Event_Time_Date == i) %>%  # needed to do this each day (3-7) because the complete file was too large to do at once
  left_join(RouteMinMax,
            by = c("Route" = "Route",
                   "Event_Time_Date" = "Event_Time_Date"
                  )
           ) %>% 
  mutate(SampTime = as_datetime(runif(nrow(.), #200000,
                                      min = MinTime,
                                      max = MaxTime
                                     ),
                                tz = "America/New_York"
                               )
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         ) 

# str(Samp)
# View(head(Samp, 500))
# 
# View(
# group_by(Samp,
#          RowNum_OG
#         ) %>%
#   summarise(Cnt_Num = n(),
#             Cnt_Pct = 100 * Cnt_Num / nrow(Samp)
#            ) %>%
#   arrange(desc(Cnt_Num))
# )


# For each Route and StopID combination, get all the Event_Time values that are after the SampTime value.
# estimating approx 2hrs of runtime for all 2.8m records
Testing_A <- sqldf("   Select               t1.*
                                            ,t2.Event_Time             as NextBus
                        From                 Samp                      as t1
                             Inner Join      Samp                      as t2
                                On              t1.Route = t2.Route
                                And             t1.StopID_Clean = t2.StopID_Clean
                                And             t2.Event_Time > t1.SampTime
                        Order By             t1.Route
                                            ,t1.StopID_Clean
                                            ,t1.Event_Time
                                            ,t2.Event_Time
                  "
                 ) %>% 
  mutate(NB = as_datetime(NextBus,
                          tz = "America/New_York"
                         )
        )

# str(Testing_A)
# View(head(Testing_A, 500))
# View(head(Samp, 500))


# Filter the dataframe to only include the bus arrival at StopID that is the next to come after the SampTime.
# estimating approx 20min of runtime for all 2.8m records
Testing <- select(Testing_A,
                  -NextBus
                 ) %>% 
  group_by(RowNum_OG) %>% 
  filter(NB == min(NB)
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         ) %>% 
  mutate(WaitTime_Min = as.numeric(NB - SampTime),
         WaitTime_Sec = WaitTime_Min * 60,
         WaitTime_Sec2 = NB - SampTime,
         WaitTime_Min2 = WaitTime_Sec2 / 60
        ) %>% 
  as.data.frame()

assign(paste0("Testing_", i),
       Testing
      )

rm(Samp,Testing_A, Testing)
str(get(paste0("Testing_", i)))
View(get(paste0("Testing_", i)))
}


# Bind all the individual dataframes together.
WaitData_DayPull <- bind_rows(Testing_3,
                              Testing_4,
                              Testing_5,
                              Testing_6,
                              Testing_7
                             ) %>% 
  mutate(WaitTime_Sec3 = NB - SampTime,
         WaitTime_Min3 = WaitTime_Sec3 / 60
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         )


# saveRDS(WaitData_DayPull, "WaitData_DayPull")

```


```{r}

rm(Testing_3, Testing_4, Testing_5, Testing_6, Testing_7)


WaitData_DayPull <- readRDS("WaitData_DayPull")
str(WaitData_DayPull)

View(head(WaitData_DayPull, 500))
View(tail(WaitData_DayPull, 500))

```


Waiting time analyses.

Munging and sampling data to go from time beteen buses to "average" waiting time.

Basic investigation of any missing rows from data pulled by day.
```{r}

DistinctRowNum_OG <- distinct(select(WaitData_DayPull,
                                     RowNum_OG
                                    )
                             )

str(DistinctRowNum_OG)

# View(
# anti_join(Samp,
#           DistinctRowNum_OG,
#           by = c("RowNum_OG" = "RowNum_OG")
#          )
# )


# The samp time is AFTER the last bus passed that StopID_Clean
# View(filter(Samp,
#             Event_Time > "2016-10-07 19:48:41" &
#               Route == "X2" &
#               StopID_Clean == 1003774
#            )
#     )

# Next Bus (NB) can be on the next morning
# View(filter(Testing7,
#             SampTime > "2016-10-06 23:58:00" &
#               SampTime < "2016-10-06 23:59:59")
#     )

```


Waiting time analyses.

Munging and sampling data to go from time beteen buses to "average" waiting time.

(Pulls here are done by groupings of bus routes, as the data are too large to do at once.)

First, we need to find the most common bus routes.
```{r}

rm(DistinctRowNum_OG)


# View(head(NewTravTime, 500))

set.seed(123456789)
BusGroups <- group_by(NewTravTime,
                      Route
                     ) %>% 
  summarise(Cnt_Num = n(),
            Cnt_Pct = Cnt_Num / nrow(NewTravTime)
           ) %>% 
  arrange(desc(Cnt_Num)
         ) %>% 
  mutate(RowNum = row_number(),
         RandNum = runif(n = 268),
         RouteGroup = ifelse(RandNum <= 0.2,
                             1,
                      ifelse(RandNum <= 0.4,
                             2,
                      ifelse(RandNum <= 0.6,
                             3,
                      ifelse(RandNum <= 0.8,
                             4,
                             5
                            ))))
        )

str(BusGroups)
View(BusGroups)
summary(BusGroups)
head(BusGroups, 50)

```


Waiting time analyses.

Munging and sampling data to go from time beteen buses to "average" waiting time.

(Pulls here are done by groupings of bus routes, as the data are too large to do at once.)
```{r eval=FALSE}

# View(head(NewTravTime, 500))

# For each record, create a random datetime between the first and last stop for that bus route (on that day).
for(i in 1:5){
  
set.seed(123456789)
Samp <- left_join(NewTravTime,
                  BusGroups,
                  by = c("Route" = "Route")
                  ) %>% 
  select(RowNum_OG,
         Route,
         RouteGroup,
         Event_Time_Date,
         StopID_Clean,
         starts_with("Event")
        ) %>% 
  filter(RouteGroup == i) %>%  # needed to do this each RouteGroup (1-5) because the complete file was too large to do at once
  left_join(RouteMinMax,
            by = c("Route" = "Route",
                   "Event_Time_Date" = "Event_Time_Date"
                  )
           ) %>% 
  mutate(SampTime = as_datetime(runif(nrow(.), #200000,
                                      min = MinTime,
                                      max = MaxTime
                                     ),
                                tz = "America/New_York"
                               )
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         ) 

# str(Samp)
# View(head(Samp, 500))
# 
# View(
# group_by(Samp,
#          RowNum_OG
#         ) %>%
#   summarise(Cnt_Num = n(),
#             Cnt_Pct = 100 * Cnt_Num / nrow(Samp)
#            ) %>%
#   arrange(desc(Cnt_Num))
# )


# For each Route and StopID combination, get all the Event_Time values that are after the SampTime value.
# estimating approx 2hrs of runtime for all 2.8m records
Testing_A <- sqldf("   Select               t1.*
                                            ,t2.Event_Time             as NextBus
                        From                 Samp                      as t1
                             Inner Join      Samp                      as t2
                                On              t1.Route = t2.Route
                                And             t1.StopID_Clean = t2.StopID_Clean
                                And             t2.Event_Time > t1.SampTime
                        Order By             t1.Route
                                            ,t1.StopID_Clean
                                            ,t1.Event_Time
                                            ,t2.Event_Time
                  "
                 ) %>% 
  mutate(NB = as_datetime(NextBus,
                          tz = "America/New_York"
                         )
        )

# str(Testing_A)
# View(head(Testing_A, 500))
# View(head(Samp, 500))


# Filter the dataframe to only include the bus arrival at StopID that is the next to come after the SampTime.
# estimating approx 20min of runtime for all 2.8m records
Testing <- select(Testing_A,
                  -NextBus
                 ) %>% 
  group_by(RowNum_OG) %>% 
  filter(NB == min(NB)
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         ) %>% 
  mutate(WaitTime_Min = as.numeric(NB - SampTime),
         WaitTime_Sec = WaitTime_Min * 60
        ) %>% 
  as.data.frame()

assign(paste0("Testing", i),
       Testing
      )

rm(Samp,Testing_A, Testing)
str(get(paste0("Testing", i)))
View(get(paste0("Testing", i)))
}


# Bind all the individual dataframes together.
WaitData_RoutePull <- bind_rows(Testing1,
                                Testing2,
                                Testing3,
                                Testing4,
                                Testing5
                             ) %>% 
  mutate(WaitTime_Sec2 = NB - SampTime,
         WaitTime_Min2 = WaitTime_Sec2 / 60
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         )


# saveRDS(WaitData_RoutePull, "WaitData_RoutePull")

```


```{r}

rm(BusGroups, i, Testing3, Testing4, Testing5, Testing6, Testing7)


WaitData_RoutePull <- readRDS("WaitData_RoutePull")
str(WaitData_RoutePull)

View(head(WaitData_RoutePull, 500))
View(tail(WaitData_RoutePull, 500))

```


Waiting time analyses.

Munging and sampling data to go from time beteen buses to "average" waiting time.

Compare WaitData pulled by day and pulled by route.
```{r}

dim(WaitData_RoutePull)
dim(WaitData_DayPull)
nrow(WaitData_RoutePull) - nrow(WaitData_DayPull)

WaitData_Diff <- anti_join(WaitData_RoutePull,
                           WaitData_DayPull,
                           by = c("RowNum_OG" = "RowNum_OG"
                                 )
                          ) %>% 
  select(-WaitTime_Min,
         -WaitTime_Sec
        )

str(WaitData_Diff)
View(head(WaitData_Diff, 500))

View(filter(WaitData_RoutePull,
            Route == "Z8" &
              StopID_Clean == 2005465
            # RowNum_OG = 2902760
            # Event_Time = 2016-10-07 19:51:47
           )
    )

View(group_by(WaitData_Diff,
              Route
             ) %>% 
       summarise(Cnt_Num = n(),
                 Cnt_Pct = Cnt_Num / nrow(WaitData_Diff)
                ) %>% 
       arrange(desc(Cnt_Num)
              )
    )

View(filter(WaitData_Diff,
            Route == "S1"
           )
    )

View(filter(WaitData_RoutePull,
            Route == "S1" &
              StopID_Clean == 1003132
            # RowNum_OG = 1151770
            # Event_Time = 2016-10-07 09:07:12
           )
    )

# Can't tell why the pull by day has less records than the pull by route

```


Waiting time analyses.

Munging and sampling data to go from time beteen buses to "average" waiting time.

Compare WaitData (pulled by route) and original data (NewTravTime).
```{r}

dim(NewTravTime)  # 2,809,529 rows
dim(WaitData_RoutePull)  # 2,780,848 rows
nrow(NewTravTime) - nrow(WaitData_RoutePull)  # is 28,681 rows

str(select(NewTravTime,
           -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )
str(WaitData_RoutePull)

Compare_NTT_WD <- left_join(NewTravTime,
                            select(WaitData_RoutePull,
                                   RowNum_OG,
                                   # Route,
                                   RouteGroup,
                                   # StopID_Clean,
                                   # Event_Time,
                                   MinTime,
                                   MaxTime,
                                   SampTime,
                                   NB,
                                   WaitTime_Sec2,
                                   WaitTime_Min2
                                  ),
                            by = c("RowNum_OG" = "RowNum_OG")
                           ) %>% 
  select(-matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
        ) %>% 
  arrange(Route,
          StopID_Clean,
          Event_Time
         )

str(Compare_NTT_WD)  # 2,810,109 rows overall  --  29,261 rows with no match
View(head(Compare_NTT_WD, 500))
View(filter(Compare_NTT_WD,
            is.na(MinTime)
           )
    )



# View(anti_join(Samp,
#                distinct(select(WaitData_RoutePull,
#                                RowNum_OG
#                               )
#                        ),
#                by = c("RowNum_OG" = "RowNum_OG")
#               )
#     )

# The SampTime is AFTER the last bus passed that StopID_Clean
# View(filter(Samp,
#               Route == "X2" &
#               StopID_Clean == 1003774
#             # RowNum_OG = 1146723
#             # Event_Time = 2016-10-07 15:32:18
#            )
#     )

```


Clean up the data a bit.
```{r warning=FALSE}

rm(BusGroups, RouteMinMax, Samp, Testing1, Testing2, Testing3, Testing4, Testing5, Testing_3, Testing_4, Testing_5, Testing_6, Testing_7, WaitData_DayPull, WaitData_Diff)


str(Compare_NTT_WD)
View(head(Compare_NTT_WD, 500))
View(head(mutate(Compare_NTT_WD,
                 WT_Min = as.numeric(WaitTime_Min2)
                )
         )
    )

WaitTime_AsNum <- Compare_NTT_WD %>% 
  mutate(RouteStop_ID = factor(paste(Route, StopID_Clean, sep = "__")
                              )
        )
WaitTime_AsNum$WaitTime_Sec2 <- as.numeric(WaitTime_AsNum$WaitTime_Sec2)
WaitTime_AsNum$WaitTime_Min2 <- as.numeric(WaitTime_AsNum$WaitTime_Min2)

rm(Compare_NTT_WD)
str(WaitTime_AsNum)

```


General exploration of wait times.
```{r}

summary(WaitTime_AsNum$WaitTime_Min2)

```


General exploration of wait times.
```{r}

WT_Quantiles <- as.data.frame(quantile(WaitTime_AsNum$WaitTime_Min2,
                                       probs = seq(0, 1, 0.01),
                                       na.rm = TRUE
                                      )
                             )

colnames(WT_Quantiles) <- "Value_Min"

WT_Quantiles$Value_Sec = format(round(WT_Quantiles$Value_Min * 60,
                                      digits = 2
                                     ),
                                nsmall = 2
                               )
WT_Quantiles$Value_Hr = format(round(WT_Quantiles$Value_Min / 60,
                                     digits = 2
                                    ),
                                nsmall = 2
                               )
WT_Quantiles$Value_Min = format(round(WT_Quantiles$Value_Min,
                                      digits = 2
                                     ),
                                nsmall = 2
                               )

WT_Quantiles$Quantile <- seq(0, 1, 0.01)

WT_Quantiles <- select(WT_Quantiles,
                       Quantile,
                       Value_Sec,
                       Value_Min,
                       Value_Hr
                      )

str(WT_Quantiles)
View(WT_Quantiles)
WT_Quantiles


View(arrange(WaitTime_AsNum,
             desc(WaitTime_Min2)
            ) %>% 
       head(., 5000)
    )

View(filter(WaitTime_AsNum,
            between(WaitTime_Min2, 60, 200)
           ) %>% 
       arrange(desc(WaitTime_Min2)
              ) 
     # %>% 
     #   head(., 5000)
    )

# Example of extreme wait times
View(filter(WaitTime_AsNum,
            Route == "W13" &  # only 2 bus passes in the entire dataset
              StopID_Clean == 1003728
            # Event_Time = 2016-10-03 08:42:46
           )
    )

# Example of extreme wait times
View(filter(WaitTime_AsNum,
            Route == "S41" &  # only 4 bus passes in the entire dataset
              StopID_Clean == 1001095
            # Event_Time = 2016-10-05 15:41:47
           )
    )

# Example of extreme wait times
View(filter(WaitTime_AsNum,
            Route == "D8" &  # route has VERY limited service after midnight
              StopID_Clean == 1001669
            # Event_Time = 2016-10-06 20:31:16
           )
    )

```


Looks like there might be an issue in wait times when very few Route-Stop combinations are included in the dataset.  Let's explore these.
```{r}

RouteStop_Cnts <- group_by(WaitTime_AsNum,
                           RouteStop_ID
                          ) %>% 
  summarise(RouteStop_CntNum = n(),
            RouteStop_CntPct = RouteStop_CntNum / nrow(WaitTime_AsNum)
           ) %>% 
  arrange(RouteStop_CntNum)

View(RouteStop_Cnts)


RouteStop_CntOfCnt <- group_by(RouteStop_Cnts,
                               RouteStop_CntNum
                              ) %>% 
  summarise(RouteStopCnt_CntNum = n(),
            RouteStopCnt_CntPct = RouteStopCnt_CntNum / nrow(RouteStop_Cnts)
           ) %>% 
  mutate(RouteStopCnt_CntPct_CumSum = cumsum(RouteStopCnt_CntPct),
         x = 1 - RouteStopCnt_CntPct_CumSum
        ) %>% 
  arrange(RouteStop_CntNum)
  
 View(RouteStop_CntOfCnt)
 RouteStop_CntOfCnt

```


Histogram of the counts of Route-StopID combinations.
```{r}

RouteStop_Cnts_Bar <- ggplot(RouteStop_CntOfCnt,
                             aes(x = RouteStop_CntNum,
                                 # y = ..density..
                                 y = RouteStopCnt_CntNum
                                )
                            ) +
  # geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_col(fill = "lightblue", colour = "grey60", size = 0.2) +
  coord_cartesian(xlim = c(0, 500)
                  # ylim = c(0, 0.02)
                 ) +
  labs(title = "Variation in Routes Passing a Specific Stop",
       x = "Occurrences of Route-StopID Combiantions",
       y = "Counts"
      )

RouteStop_Cnts_Bar

```


Create a new dataset limiting extremely small counts of Route-StopID combinations.
```{r}

WaitTime_RteCnts <- left_join(WaitTime_AsNum,
                              RouteStop_Cnts,
                              by = c("RouteStop_ID" = "RouteStop_ID")
                             ) %>% 
  select(-RouteStop_CntPct)

dim(WaitTime_AsNum)
dim(WaitTime_RteCnts)

rm(WaitTime_AsNum)
str(WaitTime_RteCnts)


# Total rows
nrow(WaitTime_RteCnts)

# Rows of rare RouteStops
nrow(filter(WaitTime_RteCnts,
            RouteStop_CntNum <= 60
           )
    ) / nrow(WaitTime_RteCnts)

# Rows of extremely long wait times
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 180
           )
    )

nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 60
           )
    ) / nrow(WaitTime_RteCnts)

nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 120
           )
    ) / nrow(WaitTime_RteCnts)

nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 180
           )
    ) / nrow(WaitTime_RteCnts)

nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 > 240
           )
    ) / nrow(WaitTime_RteCnts)


message("All records")
select(WaitTime_RteCnts,
       WaitTime_Min2
      ) %>% 
  summary()

message("12 passes per day in a 5-day dataset")
filter(WaitTime_RteCnts,
       RouteStop_CntNum > 60  # 12 passes per day in a 5-day dataset
      ) %>% 
  select(WaitTime_Min2) %>% 
  summary()

message("<180min. >=180min, probably means something went wrong")
filter(WaitTime_RteCnts,
       WaitTime_Min2 < 180  # probably means that something went wrong
      ) %>% 
  select(WaitTime_Min2) %>% 
  summary()

```


Compare quantiles in the limited datasets.
```{r}

a <- as.data.frame(select(WaitTime_RteCnts,
                          WaitTime_Min2
                         ) %>% 
                     quantile(probs = seq(0, 1, 0.01), na.rm = TRUE)
                  )

b <- as.data.frame(filter(WaitTime_RteCnts,
                          RouteStop_CntNum > 60
                         ) %>% 
                     select(WaitTime_Min2) %>% 
                     quantile(probs = seq(0, 1, 0.01), na.rm = TRUE)
                  )

c <- as.data.frame(filter(WaitTime_RteCnts,
                          WaitTime_Min2 < 180
                         ) %>% 
                     select(WaitTime_Min2) %>% 
                     quantile(probs = seq(0, 1, 0.01), na.rm = TRUE)
                  )

WT_Filter_Quantiles <- bind_cols(a, b, c) %>% 
  mutate(Quantile = seq(0, 1, 0.01)
        )

colnames(WT_Filter_Quantiles) <- c("All", "RteStpAbv60", "WTBlw180", "Quantile")
rm(a, b, c)
View(WT_Filter_Quantiles)
WT_Filter_Quantiles

```


Histogram of all wait times.
```{r}

WaitTime_AllBus_HistDen <- ggplot(filter(select(WaitTime_RteCnts,
                                                WaitTime_Min2
                                               ),
                                         !is.na(WaitTime_Min2)
                                        ),
                                  aes(x = WaitTime_Min2,
                                      y = ..density..
                                     )
                                ) +
  geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  scale_x_continuous(breaks = seq(0, 300, 30)
                    ) +
  coord_cartesian(xlim = c(0, 300),
                  ylim = c(0, 0.035)
                 ) +
  labs(title = "Variation in Wait Time",
       x = "Wait Time (min)",
       y = "Density"
      )

WaitTime_AllBus_HistDen

```


Box plots for WaitTime (all busses, by Zip Code).
```{r}

# Count_Values is needed to display the medians on the box plots
BusRoute <- select(WaitTime_RteCnts,
                   Route,
                   WaitTime_Min2,
                   Stop_Zip
                  ) %>% 
  filter(Route == "X2")

CountValues_AllBus_Zip <- ddply(BusRoute,
                                .(Stop_Zip),
                                summarise,
                                Value_Counts = median(WaitTime_Min2, na.rm = TRUE)
                               )

WaitTime_AllBus_Zip_Box <- ggplot(BusRoute,
                                  aes(factor(Stop_Zip),
                                      WaitTime_Min2,
                                      fill = factor(Stop_Zip)
                                     )
                                 ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE, na.rm = TRUE) +
  geom_text(data = CountValues_AllBus_Zip,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 3,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 45)
                 ) +
  labs(title = "Waiting Time at a Given Stop (for the X2)",
       x = "Zip Code of Destination",
       y = "Waiting Time (min)"
      )

WaitTime_AllBus_Zip_Box

```


Test investigation of just the X2 Route. Violin plots for time between bus arrivals (by Zip Code).
```{r}

WaitTime_AllBus_Zip_Violin <- ggplot(BusRoute,
                                     aes(factor(Stop_Zip),
                                         WaitTime_Min2,
                                         fill = factor(Stop_Zip)
                                        )
                                    ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = CountValues_AllBus_Zip,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 3.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 45)
                 ) +
  labs(title = "Waiting Time at a Given Stop (for the X2)",
       x = "Zip Code of Destination",
       y = "Waiting Time (min)"
      )

TimeBtwEvents_X2_ViolinPlot_z

```


Box plots for WaitTime (Zip Code, by HourGroupZip).
```{r}

# Count_Values is needed to display the medians on the box plots
Zip <- select(WaitTime_RteCnts,
              Route,
              WaitTime_Min2,
              Stop_Zip,
              Event_Time_HrGroup
             ) %>% 
  filter(Stop_Zip == 20002)

CountValues_AllBus_HG <- ddply(Zip,
                               .(Event_Time_HrGroup),
                               summarise,
                               Value_Counts = median(WaitTime_Min2,
                                                     na.rm = TRUE
                                                    )
                               )

WaitTime_AllBus_HG_Box <- ggplot(Zip,
                                 aes(factor(Event_Time_HrGroup),
                                     WaitTime_Min2,
                                     fill = factor(Event_Time_HrGroup)
                                    )
                                ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE, na.rm = TRUE) +
  geom_text(data = CountValues_AllBus_HG,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 45)
                 ) +
  labs(title = "Waiting Time at a Given Stop (for Zip 20002)",
       x = "Hour Group",
       y = "Waiting Time (min)"
      )
  # facet_wrap(~Stop_Zip
  #            # nrow = 5
  #           )

WaitTime_AllBus_HG_Box

```


Violin plots for WaitTime (Zip Code, by HourGroupZip).
```{r}

WaitTime_AllBus_HG_Vln <- ggplot(Zip,
                                 aes(factor(Event_Time_HrGroup),
                                     WaitTime_Min2,
                                     fill = factor(Event_Time_HrGroup)
                                    )
                                ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = CountValues_AllBus_HG,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 90)
                 ) +
  labs(title = "Waiting Time at a Given Stop (for Zip 20002)",
       x = "Hour Group",
       y = "Waiting Time (min)"
      )
  # facet_wrap(~Stop_Zip
  #            # nrow = 5
  #           )

WaitTime_AllBus_HG_Vln

```


Box plots for WaitTime (Route, by HourGroupZip).
```{r}

# Count_Values is needed to display the medians on the box plots
Rte <- select(WaitTime_RteCnts,
              Route,
              WaitTime_Min2,
              Stop_Zip,
              Event_Time_HrGroup
             ) %>% 
  filter(Route == "X2")

CountValues_AllBus_RteHG <- group_by(Rte,
                                     Event_Time_HrGroup
                                    ) %>% 
  summarise(
    Value_Counts = median(WaitTime_Min2,
                          na.rm = TRUE
                         ),
    VC = quantile(WaitTime_Min2, probs = 0.9, na.rm = TRUE)
    )


WaitTime_AllBus_RteHG_Box <- ggplot(Rte,
                                    aes(factor(Event_Time_HrGroup),
                                        WaitTime_Min2,
                                        fill = factor(Event_Time_HrGroup)
                                       )
                                   ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE, na.rm = TRUE) +
  geom_text(data = CountValues_AllBus_RteHG,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, max(CountValues_AllBus_RteHG$VC))
                 ) +
  labs(title = "Waiting Time at a Given Stop",
       subtitle = ("Route X2"),
       x = "Hour Group",
       y = "Waiting Time (min)"
      ) 
# +
#   facet_wrap(~Stop_Zip
#              # nrow = 5
#             )

WaitTime_AllBus_RteHG_Box

```


Violin plots for WaitTime (Zip Code, by HourGroupZip).
```{r}

WaitTime_AllBus_RteHG_Vln <- ggplot(Rte,
                                    aes(factor(Event_Time_HrGroup),
                                        WaitTime_Min2,
                                        fill = factor(Event_Time_HrGroup)
                                       )
                                   ) + 
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
              trim = TRUE,
              scale = "count",
              na.rm = TRUE,
              show.legend = NA,
              inherit.aes = TRUE
             ) +
  geom_text(data = CountValues_AllBus_RteHG,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 2.5,
            vjust = -0.5
           ) +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  coord_cartesian(# xlim = c(0, 180),
                  ylim = c(0, 45)
                 ) +
  labs(title = "Waiting Time at a Given Stop",
       subtitle = ("(Route X2)"),
       x = "Hour Group",
       y = "Waiting Time (min)"
      ) +
  facet_wrap(~Stop_Zip
             # nrow = 5
            )

WaitTime_AllBus_RteHG_Vln

```


X2 Percentiles Line Graph Test.
```{r}

X2_Pct <- select(WaitTime_RteCnts,
                 Route,
                 Stop_Zip,
                 Event_Time_Date,
                 Event_Time_Day,
                 Event_Time_HrGroup,
                 Event_Time_Hr,
                 Latitude,
                 Longitude,
                 WaitTime_Min2
                ) %>% 
  filter(Route == "X2") %>% 
  group_by(Event_Time_Hr,
           Stop_Zip
          ) %>% 
  summarise(Pct50 = quantile(WaitTime_Min2, probs = 0.5, na.rm = TRUE),
            Pct60 = quantile(WaitTime_Min2, probs = 0.6, na.rm = TRUE),
            Pct70 = quantile(WaitTime_Min2, probs = 0.7, na.rm = TRUE),
            Pct80 = quantile(WaitTime_Min2, probs = 0.8, na.rm = TRUE),
            Pct90 = quantile(WaitTime_Min2, probs = 0.9, na.rm = TRUE)
           )

str(X2_Pct)
View(X2_Pct)


X2_Long <- gather(X2_Pct,
                  key = Percentile,
                  value = Pctile,
                  Pct50,
                  Pct60,
                  Pct70,
                  Pct80,
                  Pct90
                )

str(X2_Long)
View(X2_Long)


X2_WaitByHr_Line <- ggplot(X2_Long,
                           aes(x = Event_Time_Hr,
                               y = Pctile,
                               factor(Percentile),
                               color = Percentile
                              )
                          ) +
  geom_line() +
  theme(legend.title=element_blank(),
        legend.position = "bottom"
       ) +
  coord_cartesian(xlim = c(0, 23)
                  # ylim = c(0, 45)
                 ) + 
  scale_x_continuous(breaks = seq(0, 23, 2)
                    ) +
  labs(title = "Waiting Time Throughout the Day",
       subtitle = ("(Route X2)"),
       x = "Hour of the Day",
       y = "Waiting Time (min)"
      ) +
  facet_wrap(~Stop_Zip)

X2_WaitByHr_Line

```



GET DATA READY FOR SHINY  --  GET DATA READY FOR SHINY  --  GET DATA READY FOR SHINY
GET DATA READY FOR SHINY  --  GET DATA READY FOR SHINY  --  GET DATA READY FOR SHINY
GET DATA READY FOR SHINY  --  GET DATA READY FOR SHINY  --  GET DATA READY FOR SHINY

BaseData: Used in plots by hour and zipcode (first two Shiny tabs).
```{r}

# str(WaitTime_RteCnts)
nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 60
           )
    ) / nrow(WaitTime_RteCnts)

nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 120
           )
    ) / nrow(WaitTime_RteCnts)

nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 180
           )
    )

nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 180
           )
    ) / nrow(WaitTime_RteCnts)

nrow(filter(WaitTime_RteCnts,
            WaitTime_Min2 <= 240
           )
    ) / nrow(WaitTime_RteCnts)

Shiny_WaitData_Base <- select(WaitTime_RteCnts,
                              Route,
                              Stop_Zip,
                              Event_Time,
                              Event_Time_Date,
                              Event_Time_Day,
                              Event_Time_HrGroup,
                              Event_Time_Hr,
                              Latitude,
                              Longitude,
                              WaitTime_Min2
                             ) %>% 
  mutate(Event_Time_YrMthDayHr = floor_date(Event_Time, "hour")
        ) %>% 
  rename(ZipCode = Stop_Zip,
         HourGroup = Event_Time_HrGroup,
         Date = Event_Time_Date,
         Day = Event_Time_Day,
         Hour = Event_Time_Hr,
         WaitTime_Min = WaitTime_Min2
        ) %>% 
  filter(WaitTime_Min <= 180)

Shiny_WaitData_Base$Route <- factor(Shiny_WaitData_Base$Route)

str(Shiny_WaitData_Base)
View(tail(Shiny_WaitData_Base, 500))

saveRDS(Shiny_WaitData_Base,
        "Shiny_WaitData_Base"
       )

# Shiny_WaitData_Base <- readRDS("Shiny_WaitData_Base")

```


Prep data for mapping.

Load and prep the Zip Code shapefile.  The shapefile was originally obtained from [data.gov](https://catalog.data.gov/dataset/tiger-line-shapefile-2016-2010-nation-u-s-2010-census-5-digit-zip-code-tabulation-area-zcta5-na).
```{r message = FALSE, warning = FALSE}

# devtools::install_github("dkahle/ggmap")
# devtools::install_github("hadley/ggplot2")
# install.packages("ggmap", type = "source")

# devtools::install_github('hadley/ggplot2')
# devtools::install_github("hadley/ggplot2@v2.2.0")
# devtools::install_github('thomasp85/ggforce')
# devtools::install_github('thomasp85/ggraph')
# devtools::install_github('slowkow/ggrepel')


# shapefile originally obtained from: https://catalog.data.gov/dataset/tiger-line-shapefile-2016-2010-nation-u-s-2010-census-5-digit-zip-code-tabulation-area-zcta5-na
tract <- 
  readOGR(dsn = paste0(BasePath, "DCMetroBus/tl_2016_us_zcta510"),
          layer = "tl_2016_us_zcta510"
         )
  
class(tract)

# convert the GEOID to a character
tract@data$GEOID <- as.character(tract@data$GEOID)
str(tract@data)


ggtract <- tidy(tract, region = "GEOID")
saveRDS(ggtract, "ggtract")
# ggtract <- readRDS("ggtract")

str(ggtract)
summary(ggtract)
# View(head(ggtract, 50))

```


Prep data for mapping.

Join the mapping data to the base data used in Shiny.
```{r}

ZipWaitTest <- filter(Shiny_WaitData_Base,
                      WaitTime_Min <= 180 &
                        !is.na(ZipCode)
                     ) %>% 
  group_by(ZipCode,
           Event_Time_YrMthDayHr
           # Event_Time_Day,
           # Event_Time_Hr
          ) %>% 
  summarise(Pct80 = quantile(WaitTime_Min, probs = 0.8, na.rm = TRUE)
           ) %>% 
  arrange(# Event_Time_Hr,
          ZipCode,
          Event_Time_YrMthDayHr
         ) %>% 
  as.data.frame() %>% 
  mutate(Event_Time_DateNew = floor_date(Event_Time_YrMthDayHr, "day"),
         Event_Time_HrNew = hour(Event_Time_YrMthDayHr),
         Pct80_Level = factor(ifelse(Pct80 < 10,
                                     "Below 10",
                              ifelse(Pct80 < 20,
                                     "Below 20",
                              ifelse(Pct80 < 30,
                                     "Below 30",
                              ifelse(Pct80 < 40,
                                     "Below 40",
                              ifelse(Pct80 < 50,
                                     "Below 50",
                              ifelse(Pct80 < 60,
                                     "Below 60",
                                     "60 Plus"
                                    )))))),
                              levels = c("Below 10", "Below 20", "Below 30", 
                                         "Below 40", "Below 50", "Below 60", "60 Plus"
                                        ),
                              ordered = TRUE
                             )
        )

str(ZipWaitTest)
ZipWaitTest$ZipCode <- as.character(ZipWaitTest$ZipCode)
str(ZipWaitTest)
summary(ZipWaitTest)

View(head(ZipWaitTest, 500))


# ggtract <- readRDS("ggtract")
StopZip_Left <- left_join(ZipWaitTest,
                          ggtract,
                          by = c("ZipCode" = "id")
                         )

str(StopZip_Left)
summary(StopZip_Left)

```


Test mapping functionaltiy.
```{r}

map <- get_map(location = c(lon = -77.03676, lat = 38.89784), #coordinates for the White House
               source = "google",
               # maptype = "roadmap"
               zoom = 12
              )

ggmap(map) +
  geom_polygon(aes(x = long, 
                   y = lat, 
                   group = group,
                   fill = Pct80_Level
                  ), 
               data = filter(StopZip_Left,
                             Event_Time_YrMthDayHr == as.POSIXct("2016-10-07 20:00:00")
                             # &
                             #   Stop_Zip == "20003"
                            ),
               colour = "gray1", 
               # fill = 'black', 
               alpha = .4, 
               size = .3
              ) +
# +
  # scale_fill_gradientn(colours = c("white", "royalblue4", "red"),
  #                      #  "lightsteelblue4",
  #                      # "lightpink1",
  #                      # values=cbPalette,
  #                      # values = c(1,0.5, .3, .2, .1, 0)
  #                      na.value = "black",
  #                      breaks = c(seq(0, 180, 30))
  #                      # values = rescale()
  #                     ) 
# +
  scale_fill_brewer(palette = "Spectral", # "YlOrRd" # "Set1",
                    direction = -1,
                    limits = levels(StopZip_Left$Pct80_Level)
                   )

```


Shiny data for mapping (used in 3rd tab).
```{r}

View(head(filter(StopZip_Left,
                 Event_Time_HrNew == 15
                ),
          500
         )
    )

Shiny_WaitData_Map <- StopZip_Left %>% 
  rename(YrMthDayHr = Event_Time_YrMthDayHr,
         YrMthDay = Event_Time_DateNew,
         Hour = Event_Time_HrNew
        )

str(Shiny_WaitData_Map)


Shiny_WaitData_Map_Wed <- filter(Shiny_WaitData_Map,
                                 YrMthDay == as.POSIXct("2016-10-05")
                                )

Shiny_WaitData_Map_Thu <- filter(Shiny_WaitData_Map,
                                 YrMthDay == as.POSIXct("2016-10-06")
                                )

str(Shiny_WaitData_Map_Wed)
summary(Shiny_WaitData_Map_Wed)


saveRDS(Shiny_WaitData_Map,
        "Shiny_WaitData_Map.rds"
       )

saveRDS(Shiny_WaitData_Map_Wed,
        "Shiny_WaitData_Map_Wed.rds"
       )

saveRDS(Shiny_WaitData_Map_Thu,
        "Shiny_WaitData_Map_Thu.rds"
       )

```




Clustering

Data prep.
```{r}

rm(tract, ggtract, StopZip_Left, ZipWaitTest, Shiny_WaitData_Base, Shiny_WaitData_Map, Shiny_WaitData_Map_Wed, Shiny_WaitData_Map_Thu)


dim(NewTravTime)
dim(WaitTime_RteCnts)


str(select(NewTravTime,
           -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )
str(select(NewTravTime,
           matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
          )
   )
str(WaitTime_RteCnts)




RouteStats <- filter(WaitTime_RteCnts,
                     WaitTime_Min2 <= 180
                    ) %>% 
  mutate(SpeedAvg_Mph_TDMNH_TTSN = TravelDistance_Mi_NewHvrs / (TT_Sec_New / 60 / 60)
        ) %>% 
  group_by(Route) %>% 
  summarise(BusDayEventNum_Mean = mean(BusDay_EventNum, na.rm = TRUE),
            BusDayEventNum_Pct10 = quantile(BusDay_EventNum, probs = 0.10, na.rm = TRUE),
            BusDayEventNum_Pct25 = quantile(BusDay_EventNum, probs = 0.25, na.rm = TRUE),
            BusDayEventNum_Pct50 = quantile(BusDay_EventNum, probs = 0.50, na.rm = TRUE),
            BusDayEventNum_Pct75 = quantile(BusDay_EventNum, probs = 0.75, na.rm = TRUE),
            BusDayEventNum_Pct90 = quantile(BusDay_EventNum, probs = 0.90, na.rm = TRUE),
            StopSequence_Mean = mean(Stop_Sequence, na.rm = TRUE),
            StopSequence_Pct10 = quantile(Stop_Sequence, probs = 0.10, na.rm = TRUE),
            StopSequence_Pct25 = quantile(Stop_Sequence, probs = 0.25, na.rm = TRUE),
            StopSequence_Pct50 = quantile(Stop_Sequence, probs = 0.50, na.rm = TRUE),
            StopSequence_Pct75 = quantile(Stop_Sequence, probs = 0.75, na.rm = TRUE),
            StopSequence_Pct90 = quantile(Stop_Sequence, probs = 0.90, na.rm = TRUE),
            EventTimeHr_Mean = mean(Event_Time_Hr, na.rm = TRUE),
            EventTimeHr_Pct10 = quantile(Event_Time_Hr, probs = 0.10, na.rm = TRUE),
            EventTimeHr_Pct25 = quantile(Event_Time_Hr, probs = 0.25, na.rm = TRUE),
            EventTimeHr_Pct50 = quantile(Event_Time_Hr, probs = 0.50, na.rm = TRUE),
            EventTimeHr_Pct75 = quantile(Event_Time_Hr, probs = 0.75, na.rm = TRUE),
            EventTimeHr_Pct90 = quantile(Event_Time_Hr, probs = 0.90, na.rm = TRUE),
            DwellTime2_Mean = mean(Dwell_Time2, na.rm = TRUE),
            DwellTime2_Pct10 = quantile(Dwell_Time2, probs = 0.10, na.rm = TRUE),
            DwellTime2_Pct25 = quantile(Dwell_Time2, probs = 0.25, na.rm = TRUE),
            DwellTime2_Pct50 = quantile(Dwell_Time2, probs = 0.50, na.rm = TRUE),
            DwellTime2_Pct75 = quantile(Dwell_Time2, probs = 0.75, na.rm = TRUE),
            DwellTime2_Pct90 = quantile(Dwell_Time2, probs = 0.90, na.rm = TRUE),
            TravDistMi_Mean = mean(TravelDistance_Mi_NewHvrs, na.rm = TRUE),
            TravDistMi_Pct10 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.10, na.rm = TRUE
                                       ),
            TravDistMi_Pct25 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.25, na.rm = TRUE
                                       ),
            TravDistMi_Pct50 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.50, na.rm = TRUE
                                       ),
            TravDistMi_Pct75 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.75, na.rm = TRUE
                                       ),
            TravDistMi_Pct90 = quantile(TravelDistance_Mi_NewHvrs,
                                        probs = 0.90, na.rm = TRUE
                                       ),
            TravTimSec_Mean = mean(TT_Sec_New, na.rm = TRUE),
            TravTimSec_Pct10 = quantile(TT_Sec_New, probs = 0.10, na.rm = TRUE),
            TravTimSec_Pct25 = quantile(TT_Sec_New, probs = 0.25, na.rm = TRUE),
            TravTimSec_Pct50 = quantile(TT_Sec_New, probs = 0.50, na.rm = TRUE),
            TravTimSec_Pct75 = quantile(TT_Sec_New, probs = 0.75, na.rm = TRUE),
            TravTimSec_Pct90 = quantile(TT_Sec_New, probs = 0.90, na.rm = TRUE),
            WaitTimMin_Mean = mean(WaitTime_Min2, na.rm = TRUE),
            WaitTimMin_Pct10 = quantile(WaitTime_Min2, probs = 0.10, na.rm = TRUE),
            WaitTimMin_Pct25 = quantile(WaitTime_Min2, probs = 0.25, na.rm = TRUE),
            WaitTimMin_Pct50 = quantile(WaitTime_Min2, probs = 0.50, na.rm = TRUE),
            WaitTimMin_Pct75 = quantile(WaitTime_Min2, probs = 0.75, na.rm = TRUE),
            WaitTimMin_Pct90 = quantile(WaitTime_Min2, probs = 0.90, na.rm = TRUE)
           ) %>% 
  as.data.frame()

str(RouteStats)

rownames(RouteStats) <- RouteStats$Route
str(RouteStats)
View(RouteStats)


RouteStats_Scaled <- select(RouteStats,
                            -Route
                           ) %>% 
  scale()

str(RouteStats_Scaled)
class(RouteStats_Scaled)
View(RouteStats_Scaled)

message("RouteStats")
summary(RouteStats)

message("RouteStats_Scaled")
summary(RouteStats_Scaled)

```


PCA

Using caret::preProcess.
```{r}

str(RouteStats)

Trnsfrm <- preProcess(select(RouteStats,
                             -Route
                            ),
                      method = c("BoxCox", "center", "scale", "pca")
                     )

# loadings
Trnsfrm$rotation

RouteStats_Pca <- predict(Trnsfrm, RouteStats) %>% 
  select(-Route)

View(RouteStats_Pca)

str(RouteStats_Pca)
head(RouteStats_Pca)

```


PCA

Using stats::prcomp.
```{r}

str(RouteStats)

PcaRes <- prcomp(select(RouteStats,
                        -Route
                       ),
                 center = TRUE,
                 scale. = TRUE
                )

str(PcaRes)

head(unclass(PcaRes$rotation))


PcaRes_Vars <- get_pca_var(PcaRes)
PcaRes_Vars
# Where variables lie in relation to the eigenvectors
PcaRes_Vars$coord

# Graph of the Factor-Variable Map
fviz_pca_var(PcaRes,
             col.var = "contrib"
            ) +
  scale_color_gradient2(low = "white",
                        mid = "blue", 
                        high = "red",
                        midpoint = 2
                       )

# Graph of the Factor-Variable Map (top 10 contributing variables)
fviz_pca_var(PcaRes,
             col.var = "contrib",
             select.var = list(contrib = 10)
            ) +
  scale_color_gradient2(low = "white",
                        mid = "blue", 
                        high = "red",
                        midpoint = 3.8
                       )


PcaRes_Rtes <- get_pca_ind(PcaRes)
PcaRes_Rtes
# Where routes lie in relation to the eigenvectors
PcaRes_Rtes$coord

# Graph of Route Map
fviz_pca_ind(PcaRes,
             col.ind="cos2"
            ) +
  scale_color_gradient2(low = "white",
                        mid = "blue",
                        high = "red",
                        midpoint = 0.50
                       ) +
  # comment out xlim and ylim to see EXTREME outlier Routes
  xlim(-5, 5) +
  ylim(-5, 5)

# Graph of Route Map (top 10 contributing variables)
fviz_pca_ind(PcaRes,
             col.ind="cos2",
             select.var = list(cos2 = 10)
            ) +
  scale_color_gradient2(low = "white",
                        mid = "blue",
                        high = "red",
                        midpoint = 0.50
                       ) +
  # comment out xlim and ylim to see EXTREME outlier Routes
  xlim(-5, 5) +
  ylim(-5, 5)

# Inspecting what looks to be an EXTREME outlier route
View(filter(WaitTime_RteCnts,
            Route == "SH99"
           )
    )


# Biplot of Routes and Variables
fviz_pca_biplot(PcaRes,  geom = "text") +
  xlim(-5, 5) +
  ylim(-5, 5)



# 9 eigenvalues give ~ 90% of the variance
# "elbow" at ~6th Principal Component
# ~ 8 eigenvalues > 1 (PC accounts for more variance than accounted the original standardized variables)
View(get_eigenvalue(PcaRes))
fviz_screeplot(PcaRes, ncp = 15)
fviz_screeplot(PcaRes, ncp = 15, choice = "eigenvalue")


# Create a dataframe for the "top" 8 PCs
RouteStats_Pca_8Eign <- as.data.frame(PcaRes_Rtes$coord) %>% 
  select(Dim.1,
         Dim.2,
         Dim.3,
         Dim.4,
         Dim.5,
         Dim.6,
         Dim.7,
         Dim.8
        )

View(RouteStats_Pca_8Eign)

```


Clustering (using the Principal Components computed using caret::preProcess).

Are the data clusterable?
```{r}

##### Are the data clusterable?
# gradient_col <- list(low = "steelblue", high = "white")
ClustData_Ends <- get_clust_tendency(RouteStats_Pca,
                                     n = nrow(RouteStats_Pca
                                             ) - 1,
                                     # gradient = gradient_col,
                                     seed = 123456789
                                    )

str(ClustData_Ends)

# Hopkins statistic
ClustData_Ends$hopkins_stat  # value of 0.1657494 implies that the data are not uniformly distributed (they are "clusterable")

#plot
ClustData_Ends$plot

```


Clustering. How many clusters are there?

kmeans, pam, and hierarchical clustring methods, using within sum of squares and silhouette measures.
```{r}

# class(RouteStats_Pca)

fviz_nbclust(RouteStats_Pca, kmeans, method = "wss")  # ~8 clusters
fviz_nbclust(RouteStats_Pca, pam, method = "wss")  # ~6 clusters
fviz_nbclust(RouteStats_Pca, hcut, method = "wss")  # ~6 clusters

fviz_nbclust(RouteStats_Pca, kmeans, method = "silhouette")  # 2 clusters
fviz_nbclust(RouteStats_Pca, pam, method = "silhouette")  # 2 clusters
fviz_nbclust(RouteStats_Pca, hcut, method = "silhouette",
             hc_method = "complete")  # 2 clusters

```


Clustering. How many clusters are there?

kmeans method with the gap statistic, using bootstrap.
```{r}

# Compute gap statistic
# kmeans version
set.seed(123456789)
# system.time(
gap_stat_km <- clusGap(RouteStats_Pca,
                       FUN = kmeans,
                       nstart = 25,
                       K.max = 10,
                       B = 500
                      )
# )

# Print
print(gap_stat_km, method = "Tibs2001SEmax")
print(gap_stat_km)


# pam version
set.seed(123456789)
gap_stat_pm <- clusGap(RouteStats_Pca,
                       FUN = pam,
                       K.max = 10,
                       B = 500
                      )

# Print
print(gap_stat_pm, method = "Tibs2001SEmax")
print(gap_stat_pm)


# hierarchical version
set.seed(123456789)
gap_stat_hcut <- clusGap(RouteStats_Pca,
                         FUN = hcut,
                         K.max = 10,
                         B = 500
                        )

# Print
print(gap_stat_hcut, method = "Tibs2001SEmax")
print(gap_stat_hcut)



# Plot kmeans
fviz_gap_stat(gap_stat_km, 
              maxSE = list(method = "Tibs2001SEmax")
             )  # 1 cluster

# Plot pam
fviz_gap_stat(gap_stat_pm, 
              maxSE = list(method = "Tibs2001SEmax")
             )  # 2 cluster

# Plot hierarchical
fviz_gap_stat(gap_stat_hcut, 
              maxSE = list(method = "Tibs2001SEmax")
             )  # 1 cluster

```


Clustering. How many clusters are there?

kmeans method with various different statistics.
```{r}

# str(iris)

nb <- NbClust(RouteStats_Pca, #scale(iris[ ,-5]),
              distance = "euclidean",
              min.nc = 2,
              max.nc = 15,
              method = "kmeans",
              index = "all"
             )

fviz_nbclust(nb)

```


Clustering. How many clusters are there?

Hierarchical clustering method. Particularly looking at silhouette statistics.
```{r}

# Hierarchical clustering, cut in 2 to 15 groups
for(i in 2:15) {
  assign(paste0("HCRes_K", i),
         eclust(RouteStats_Pca,
                "hclust",
                k = i,
                method = "complete",
                graph = FALSE
               )
        )
  
  assign("x",
         get(paste0("HCRes_K", i)
            )
        )
  
  assign(paste0("HCStats_K", i),
         cluster.stats(dist(RouteStats_Scaled,
                            method ="euclidean"
                           ),
                       x$cluster
                      )
        )
  
  assign("y",
         get(paste0("HCStats_K", i)
            )
        )
  
  assign(paste0("HCDend_K", i),
         fviz_dend(x, rect = TRUE, show_labels = FALSE)
        )
  
  assign(paste0("HCSil_K", i),
         fviz_silhouette(x)
        )
  
  assign(paste0("HCSilWidth_K", i),
         as.data.frame(y$clus.avg.silwidths) %>% 
           mutate(KVal = 1:nrow(.)
                 )
        )
  }


HCSilWidth_AllK <- left_join(select(HCSilWidth_K15,
                                    KVal,
                                    `y$clus.avg.silwidths`
                                   ),
                             HCSilWidth_K14,
                             by = c("KVal" = "KVal")
                            ) %>% 
  left_join(.,
            HCSilWidth_K13,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K12,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K11,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K10,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K9,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K8,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K7,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K6,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K5,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K4,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K3,
            by = c("KVal" = "KVal")
           ) %>% 
  left_join(.,
            HCSilWidth_K2,
            by = c("KVal" = "KVal")
           )
  
colnames(HCSilWidth_AllK) <- c("KVal", "K15", "K14", "K13", "K12", "K11", "K10", "K9",
                               "K8", "K7", "K6", "K5", "K4", "K3", "K2"
                              )


# Visualize
HCDend_K2
HCDend_K3
HCDend_K4
HCDend_K5
HCDend_K6
HCDend_K7
HCDend_K8
HCDend_K9
HCDend_K10
HCDend_K11
HCDend_K12
HCDend_K13
HCDend_K14
HCDend_K15

HCSil_K2
HCSil_K3
HCSil_K4
HCSil_K5
HCSil_K6
HCSil_K7
HCSil_K8
HCSil_K9
HCSil_K10
HCSil_K11
HCSil_K12
HCSil_K13
HCSil_K14
HCSil_K15


HCSilWidth_AllK

```


With Hierarchical Clustering and k=2, these are the routes in each cluster.
```{r}

HC_K2 <- eclust(RouteStats_Pca,
                "hclust",
                k = 2,
                method = "complete",
                graph = FALSE
               )

str(HC_K2)

HC_K2_Clusters <- as.data.frame(HC_K2$cluster) %>% 
  rename(ClusterNum = `HC_K2$cluster`) %>%
  mutate(BusRoute = rownames(.)
        ) %>% 
  arrange(ClusterNum,
          BusRoute
         )

HC_K2_Clusters

group_by(HC_K2_Clusters,
         ClusterNum
        ) %>% 
  summarise(Cnt = n()
          )

```


Using kmeans, PAM, and Hierarchical clustering methods, we can say we probably have 2 clusters.

Let's try density clustering.  (This tends to show that maybe there is only one "cluster," meaning that data are not clusterable.)
```{r}

rm(list = ls(pattern = "_K")
  )


# Compute DBSCAN using fpc package
kNNdistplot(RouteStats_Pca, k = 10)
abline(h = 8.5, lty = 2)

set.seed(123456789)
db <- fpc::dbscan(RouteStats_Pca,
                  eps = 8.5,
                  MinPts = 10
                )

str(db)
db

# Plot DBSCAN results
fviz_cluster(db,
             RouteStats_Pca,
             stand = FALSE,
             frame = FALSE,
             geom = "point"
            )

```


We can say that MAYBE there are two clusters, but there is more evidence for probably just one cluster (i.e., the data are NOT clusterable).
```{r message = FALSE, warning = FALSE}

# remove no longer needed items
rm(X2_Long, X2_Pct, ClustData_Ends, db, gap_stat, gap_stat_hcut, gap_stat_km, gap_stat_pm, i, nb, rd, Trnsfrm, x, y, BusRoute, Rte, map, WaitTime_AllBus_Zip_Box, WaitTime_AllBus_Zip_Violin, X2_WaitByHr_Line)

rm(list = ls(pattern = "Count")
  )

rm(list = ls(pattern = "RouteStop_")
  )

rm(list = ls(pattern = "TimeBtw")
  )

rm(list = ls(pattern = "PcaRes")
  )

rm(BasePath)

```







Investigating TravelTime_Sec.
```{r}

View(filter(TTLargeRteChng,
            !is.na(TravelTime_Sec) &
              RteChange2 == "Same"
           ) %>% 
       arrange(desc(TravelTime_Sec),
               SpeedAvg_Mph_NewHvrs
              ) %>%
       head(500)
    )


# examples where TravelTime_Sec is small (1 sec) and SpeedAvg_Mph_NewHvrs is large.
View(select(NewTravTime,
            # -matches("(q(2|5|(95)|(98)))|Mean|Med|Cnt")
            -(TD_Mi_q2:TD_Mi_SSHG_Cnt_F),
            -(TT_Hr_q2:TT_Hr_SSHG_Cnt_F)
           ) %>% 
       filter((RowNum_OG >= 2217353 & RowNum_OG <= 2217373) | # 2217363
                (RowNum_OG >= 3090321 & RowNum_OG <= 3090341) | # 3090331
                (RowNum_OG >= 80764 & RowNum_OG <= 80784) | # 80774
                (RowNum_OG >= 33840 & RowNum_OG <= 33860) # 33850
           )
    )






# examples where TravelTime_Sec is large and SpeedAvg_Mph_NewHvrs is small.
View(filter(TTLargeRteChng,
            (RowNum_OG >= 2250290 & RowNum_OG <= 2250310) | # 2250300
              (RowNum_OG >= 867717 & RowNum_OG <= 867737) | # 867727
              (RowNum_OG >= 864379 & RowNum_OG <= 864399) | # 864389
              (RowNum_OG >= 808395 & RowNum_OG <= 808415) # 808405
           )
    )
```




```{r}

         
         
# examples where TravelTime_Sec is unusually small (with TravelDistance_Mi values that are large).
View(filter(AllDays_NewTravelDist,
            (RowNum_OG >= 1042228 & RowNum_OG <= 1042248) | # 1042238
                (RowNum_OG >= 53816 & RowNum_OG <= 53836) | # 53826
                (RowNum_OG >= 360571 & RowNum_OG <= 360591) | # 360581
                (RowNum_OG >= 502271 & RowNum_OG <= 502291) # 502281 (can't explian the weird TravelTime_Sec calculation here - it's not even an integer!)
           )
    )

# still trying to explain 502281...on the day of this weirdness, the bus was only in circulation for 4-5 stops (~20 minutes) on that day (Oct 6)
View(filter(AllDays_NewTravelDist,
            Bus_ID == 2711
           )
    )


# exploring large values for TravelTime_Sec
View(filter(AllDays_NewTravelDist,
            TravelTime_Sec == 300
           ) %>% 
       arrange(desc(TravelTime_Sec),
               SpeedAvg_Mph2
              )
    )

# examples where TravelTime_Sec is unusually large (with TravelDistance_Mi values that are small, so SpeedAvg_Mph values are very small).
View(filter(AllDays_NewTravelDist,
            (RowNum_OG >= 2627459 & RowNum_OG <= 2627479) | # 2627469
                (RowNum_OG >= 2193344 & RowNum_OG <= 2193364) | # 2193354
                (RowNum_OG >= 1644123 & RowNum_OG <= 1644143) | # 1644133
                (RowNum_OG >= 869600 & RowNum_OG <= 869620) # 869610
           )
    )

```

Investigation of SpeedAvg_Mph2

View(Speed_Pctiles): 90% of SpeedAvg_Mph2 are between ~3mph and ~66mph.
```{r}

Speed_Ntile <- as.data.frame(AllDays_NewTravelDist$SpeedAvg_Mph2) %>% 
  mutate(Pctile = ntile(AllDays_NewTravelDist$SpeedAvg_Mph2, 100),
         MinR = min_rank(AllDays_NewTravelDist$SpeedAvg_Mph2),
         PctR = percent_rank(AllDays_NewTravelDist$SpeedAvg_Mph2),
         PctR_Round = round(PctR, 2)
        ) 

colnames(Speed_Ntile)[1] <- "SpeedAvg_Mph2"
str(Speed_Ntile)

Speed_Ntile_Rows <- nrow(Speed_Ntile)

View(tail(Speed_Ntile, 500))


Speed_Pctiles <- group_by(Speed_Ntile,
                          PctR_Round
                         ) %>% 
  summarise(
    MinSpeedAtPctile = min(SpeedAvg_Mph2),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / Speed_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(Speed_Pctiles)

```

Investigation of SpeedAvg_Mph2.

Exploring the removal of outlier TravelTime_Sec and TravelDistance_Mi.
```{r}

summary(select(AllDays_NewTravelDist,
               SpeedAvg_Mph,
               SpeedAvg_Mph2
              )
       )

summary(select(filter(AllDays_NewTravelDist,
                      TravelDistance_Mi > 0.0001893939 & # lowest non-zero percentile
                        TravelDistance_Mi < 1.0812500000 & # 99th percentile
                        TravelTime_Sec > 10.050000 & # 2nd percentile
                        TravelTime_Sec < 293.000000 # 98th percentile
                     ),
               SpeedAvg_Mph,
               SpeedAvg_Mph2
              )
       )

```


Investigation of SpeedAvg_Mph2.

Histogram of SpeedAvg_Mph2.
```{r}

Speed_HistDen <- ggplot(filter(AllDays_NewTravelDist,
                               !is.na(SpeedAvg_Mph2)
                              ),
                        aes(x = SpeedAvg_Mph2,
                            y = ..density..
                           )
                       ) +
  geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  stat_bin(binwidth = 5,
           geom = "text",
           size = 2.5,
           vjust = 1.5,
           aes(label = format(..count.., big.mark = ",")
              ),
          ) +
  # geom_text(aes(label = format(..count.., big.mark = ",")
  #              ),
  #           size = 3,
  #           nudge_y = (..count.. * 0.1)
  #          ) +
  coord_cartesian(xlim = c(0, 70), ylim = c(0, 0.04)
                 ) +
  #  theme(legend.position="none") +
  labs(title = "Variation in Travel Speed",
       x = "Average Speed (mph)",
       y = "Density"
      )

Speed_HistDen

```


Investigation of SpeedAvg_Mph2.

Histogram of SpeedAvg_Mph2 after removing outlier TravelTime_Sec and TravelDistance_Mi.
```{r}

View(TravDistMiNew_Pctiles)
View(TravTimeHr_Pctiles)

SpeedNoOutlier_HistDen <- ggplot(filter(AllDays_NewTravelDist,
                                        !is.na(SpeedAvg_Mph2) &
                                          TravelDistance_Mi_New > 0.077841005 & # 5th percentile
                                          # TravelDistance_Mi_New < 1.0812500000 & # 99th percentile
                                          TravelTime_Sec > 12.100000 # 4th percentile
                                          # TravelTime_Sec < 293.000000 # 98th percentile
                                       ),
                                 aes(x = SpeedAvg_Mph2,
                                     y = ..density..
                                    )
                                ) +
  geom_histogram(binwidth = 5, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  stat_bin(binwidth = 5,
           geom = "text",
           size = 2.5,
           vjust = 1.5,
           aes(label = format(..count.., big.mark = ",")
              ),
          ) +
  # geom_text(aes(label = format(..count.., big.mark = ",")
  #              ),
  #           size = 3,
  #           nudge_y = (..count.. * 0.1)
  #          ) +
  coord_cartesian(xlim = c(0, 70), ylim = c(0, 0.04)
                 ) +
  #  theme(legend.position="none") +
  labs(title = "Variation in Travel Speed",
       subtitle = "(removed low outliers of Travel Distance and Travel Time)",
       x = "Average Speed (mph)",
       y = "Density"
      )

SpeedNoOutlier_HistDen

```


Investigation of SpeedAvg_Mph2.

New dataset (NoOutliers_TravelDistNTime) when removing outlier low values of TravelDistance_Mi_New and TravelTime_Sec.
```{r}

View(TravDistMiNew_Pctiles)
View(TravTimeHr_Pctiles)

NoOutliers_TravelDistNTime <- filter(AllDays_NewTravelDist,
                                     TravelDistance_Mi_New > .077841005 & # 5th percentile
                                       # TravelDistance_Mi_New < 1.0812500000 & # 99th percentile
                                       TravelTime_Sec > 12.100000 # 4th percentile
                                       # TravelTime_Sec < 293.000000 # 98th percentile
                                    )

nrow(AllDays_NewTravelDist) - nrow(NoOutliers_TravelDistNTime)

str(NoOutliers_TravelDistNTime)
summary(NoOutliers_TravelDistNTime)

```


Investigation of SppedAvg_Mph2.

View(Speed_NoOut_Pctiles):  Aproximately 90% of SpeedAvg_Mph2 values are between ~4mph and ~56mph.
```{r}

Speed_NoOut_Ntile <- as.data.frame(NoOutliers_TravelDistNTime$SpeedAvg_Mph2) %>% 
  mutate(Pctile = ntile(NoOutliers_TravelDistNTime$SpeedAvg_Mph2, 100),
         MinR = min_rank(NoOutliers_TravelDistNTime$SpeedAvg_Mph2),
         PctR = percent_rank(NoOutliers_TravelDistNTime$SpeedAvg_Mph2),
         PctR_Round = round(PctR, 2)
        ) 

colnames(Speed_NoOut_Ntile)[1] <- "SpeedAvg_Mph2"
str(Speed_NoOut_Ntile)

Speed_NoOut_Ntile_Rows <- nrow(Speed_NoOut_Ntile)

View(tail(Speed_NoOut_Ntile, 500))


Speed_NoOut_Pctiles <- group_by(Speed_NoOut_Ntile,
                                PctR_Round
                               ) %>% 
  summarise(
    MinSpeedAtPctile = min(SpeedAvg_Mph2),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / Speed_NoOut_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(Speed_NoOut_Pctiles)

```


Investigation of SppedAvg_Mph2.

Exloring odd/impossible values.
```{r}

# Exploring when SpeedAvg_Mph2 is NA  --  does not occur at all
nrow(filter(NoOutliers_TravelDistNTime,
            is.na(SpeedAvg_Mph2)
           )
    )


# Exploring when SpeedAvg_Mph2 is zero  --  does not occur at all
nrow(filter(NoOutliers_TravelDistNTime,
            SpeedAvg_Mph2 == 0
           )
    )


# examples where SpeedAvg_Mph2 < 3.2848770
View(filter(AllDays_NewTravelDist,
            SpeedAvg_Mph2 > 0 &
              SpeedAvg_Mph2 < 3.2848770
           ) %>% 
       arrange(SpeedAvg_Mph2)
    )

# examples where SpeedAvg_Mph2 < 3.2848770
View(filter(AllDays_NewTravelDist,
            (RowNum_OG >= 485338 & RowNum_OG <= 485358) | # 485348  --  Extreme travel time, Route Change
                (RowNum_OG >= 346952 & RowNum_OG <= 346972) | # 346962  -- Extreme travel time, Route Change 
                (RowNum_OG >= 70494 & RowNum_OG <= 70514) | # 70504  --  Extreme travel time, Route Change
                (RowNum_OG >= 2051846 & RowNum_OG <= 2051866) # 2051856  --  Extreme travel time, Route Change
           )
    )

```


Investigation of SpeedAvg_Mph2.

Limit the dataset based on SpeedAvg_Mph2.
```{r}

NoOutliersSpeed <- filter(NoOutliers_TravelDistNTime,
                          between(SpeedAvg_Mph2,
                                  4.069300, # 5th percentile
                                  56.05651 #95th percentile
                                 )
                          )

nrow(NoOutliers_TravelDistNTime) - nrow(NoOutliersSpeed)

summary(NoOutliersSpeed)

```


TravelTime now looks like it has some odd values on the high end.  So let's look at those.

View(TravTime_NoOut_Pctiles):  Virtually all trips should take less than 5 minutes. (The 99th percentile of of TravelTime is approximately 8 minutes.)
```{r}

TravTime_NoOut_Ntile <- as.data.frame(NoOutliersSpeed$TravelTime_Hr) %>% 
  mutate(Pctile = ntile(NoOutliersSpeed$TravelTime_Hr, 100),
         MinR = min_rank(NoOutliersSpeed$TravelTime_Hr),
         PctR = percent_rank(NoOutliersSpeed$TravelTime_Hr),
         PctR_Round = round(PctR, 2)
        )

colnames(TravTime_NoOut_Ntile)[1] <- "TravelTime_Hr"
str(TravTime_NoOut_Ntile)

TravTime_NoOut_Ntile_Rows <- nrow(TravTime_NoOut_Ntile)

View(tail(TravTime_NoOut_Ntile, 500))


TravTime_NoOut_Pctiles <- group_by(TravTime_NoOut_Ntile,
                                   PctR_Round
                                  ) %>% 
  summarise(
    MinTravTimeHrAtPctile = min(TravelTime_Hr),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / TravTime_NoOut_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile),
         MinTravTimeSecAtPctile = MinTravTimeHrAtPctile * (60 * 60)
        )

View(TravTime_NoOut_Pctiles)

```


Investigating odd TravelTime_Sec values.

Trips longer than ~8 minutes.
```{r}

View(filter(NoOutliersSpeed,
            TravelTime_Sec > 491 # min at the 100th percentile
           ) %>% 
       arrange(desc(TravelTime_Sec)
              )
    )

# examples of TravelTime_Sec values that are largest.
View(filter(NoOutliersSpeed,
            (RowNum_OG >= 2071759 & RowNum_OG <= 2071779) | # 2071769  --  results from a route change, and a 3hr+ wait before the new route starts
                (RowNum_OG >= 1473686 & RowNum_OG <= 1473706) | # 1473696  --  results from a route change, and a 3hr wait before the new route starts
                (RowNum_OG >= 1222822 & RowNum_OG <= 1222842) | # 1222832  --  results from a route change, and a 3hr wait before the new route starts
                (RowNum_OG >= 3046089 & RowNum_OG <= 3046109) # 3046099  --  results from a route change, and a 3hr wait before the new route starts
           )
    )


# examples of TravelTime_Sec values that are the smallest of the large.
View(filter(NoOutliersSpeed,
            (RowNum_OG >= 3044689 & RowNum_OG <= 3044709) | # 3044699  --  results from a route change
                (RowNum_OG >= 3022358 & RowNum_OG <= 3022378) | # 3022368  --  results from a route change
                (RowNum_OG >= 2993016 & RowNum_OG <= 2993036) | # 2993026  --  results from a previous route change (change occurred in deleted row)
                (RowNum_OG >= 2683703 & RowNum_OG <= 2683723) # 2683713  --  results from a previous route change (change occurred in deleted row)
           )
    )

```


Let's look at the TravelTime_Sec values and route changes (DirChange2).

The 99th percentile of TravelTime_Sec for both, all trips, and just those trips NOT involving route changes (DirChange2 = "Same"), is approximately 5min (300 sec).

Nota Bene:  The percentile calculation here is defined slightly different than in most of the above analyses (which get the lowest value in the bin created by 100 ntiles).
```{r}

summary(select(NoOutliersSpeed,
               TravelTime_Sec
              )
       )

summary(select(filter(NoOutliersSpeed,
                      DirChange2 == "Same"
                     ),
               TravelTime_Sec
              )
       )

summary(select(filter(NoOutliersSpeed,
                      DirChange2 == "Change"
                     ),
               TravelTime_Sec
              )
       )


TravTimeSec_Qtiles_df <- data.frame(PctValue = seq(0, 100, 1),
                                    All = seq(1, 101, 1),
                                    Same = seq(1, 101, 1),
                                    Change = seq(1, 101, 1)
                                   )

TravTimeSec_Qtiles_df[ , 2] <- quantile(select(NoOutliersSpeed,
                                               TravelTime_Sec
                                              ),
                                        probs = seq(0, 1, 0.01),
                                        na.rm = TRUE
                                       )

TravTimeSec_Qtiles_df[ , 3] <- quantile(select(filter(NoOutliersSpeed,
                                                      DirChange2 == "Same"
                                                     ),
                                               TravelTime_Sec
                                              ),
                                        probs = seq(0, 1, 0.01),
                                        na.rm = TRUE
                                       )

TravTimeSec_Qtiles_df[ , 4] <- quantile(select(filter(NoOutliersSpeed,
                                                      DirChange2 == "Change"
                                                     ),
                                               TravelTime_Sec
                                              ),
                                        probs = seq(0, 1, 0.01),
                                        na.rm = TRUE
                                       )

View(TravTimeSec_Qtiles_df)

```


Limit the dataset now based on TravelTime_Sec.
```{r}

UpperLimitTravTime <- filter(NoOutliersSpeed,
                             TravelTime_Sec <= 491 # min at the 100th percentile
                             )

nrow(NoOutliersSpeed) - nrow(UpperLimitTravTime)

str(UpperLimitTravTime)

summary(UpperLimitTravTime)

```


Investigation of Dwell_Time2 (how long the bus is at a stop).

Differences between Dwell_Time (by WMATA) and Dwell_Time2 (by me) appear to be due to switches in RouteAlt. WMATA calculates Dwell_Time by an unknown process. The WMATA calculation is equal to my calculation, except for the records immedaitely before and after a RouteAlt switch (DirChange2).
```{r}

View(filter(AllDays_NewOrder,
            Dwell_Time != Dwell_Time2
           )
    )


# Examples where the Dwell_Time and Dwell_Time2 are different
View(filter(AllDays_NewOrder,
            ( (RowNum_OG >= 65 & RowNum_OG <= 85) | # 75
                (RowNum_OG >= 162 & RowNum_OG <= 192) | # 172
                (RowNum_OG >= 431952 & RowNum_OG <= 431972) | # 431962
                (RowNum_OG >= 434595 & RowNum_OG <= 434615) # 434605  --  this record is NOT a route switch, but does has a Sequence switch (Me: should there really be a route switch here?)
            )
           )
    )

```


Investigation of Dwell_Time2 (how long the bus is at a stop).

First, create some "rank" stats.
View(DT2_Pctiles): 95% of Dwell_Time2s are <= 23 seconds...but some weird (e.g., nearly 2 hour Dwell_Time2s exist).
```{r}

DwellTime2_Ntile <- as.data.frame(AllDays_NewOrder$Dwell_Time2) %>% 
  mutate(Pctile = ntile(AllDays_NewOrder$Dwell_Time2, 100),
         MinR = min_rank(AllDays_NewOrder$Dwell_Time2),
         PctR = percent_rank(AllDays_NewOrder$Dwell_Time2),
         PctR_Round = round(PctR, 2)
        ) 

colnames(DwellTime2_Ntile)[1] <- "Dwell_Time2"
str(DwellTime2_Ntile)

DwellTime2_Ntile_Rows <- nrow(DwellTime2_Ntile)

View(tail(DwellTime2_Ntile, 500))


DwellTime2_Pctiles <- group_by(DwellTime2_Ntile,
                               PctR_Round
                              ) %>% 
  summarise(
    MinDwellAtPctile = min(Dwell_Time2),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / DwellTime2_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(DwellTime2_Pctiles)

```


Investigation of Dwell_Time2 (how long the bus is at a stop).

Histogram of Dwell_Time2.
```{r}

DwellTime2_HistDen <- ggplot(AllDays_NewOrder, aes(x = Dwell_Time2, y = ..density..)) +
  geom_histogram(binwidth = 1, fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  coord_cartesian(xlim = c(1, 25), ylim = c(0, 0.05)
                 ) +
  xlab("Time a Bus Stays at a Stop (sec)") + 
  ylab("Density") + 
  #  theme(legend.position="none") + 
  ggtitle(expression(atop("Variation in How Long a Bus Stays at a Stop"
                          # ,atop(italic("xxxxx"),"")
                         )
                    )
         )

DwellTime2_HistDen

```


Investigation of Dwell_Time2 (how long the bus is at a stop).

Looking at some weirdly long Dwell_Time2 values.
```{r}

View(arrange(AllDays_NewOrder,
             desc(Dwell_Time2)
            )
    )


# examples of extremely large Dwell_Time2s
View(filter(AllDays_NewOrder,
            (RowNum_OG >= 292669 & RowNum_OG <= 292689) | # 292679
                (RowNum_OG >= 531057 & RowNum_OG <= 531077) | # 531067
                (RowNum_OG >= 1388627 & RowNum_OG <= 1388647) | # 1388637
                (RowNum_OG >= 1645711 & RowNum_OG <= 1645731) # 1645721
           )
    )


View(filter(AllDays_NewOrder,
            Dwell_Time2 == 0
           )
    )

```


Investigation of Delta_Time (how early or late the bus is).

View(DT2_Pctiles): 94% of Delta_Time values are between -236 seconds and 1,259 seconds. Roughly 66% of records are within 5 min late and 5 min early...but some weird (e.g., almost 50 minute late or 40 minute early) Delta_Times exist.

Note that Delta_Time is the difference from the scheduled bus arrival. So if two buses are scheduled to arrive at a destination at 10:00pm and 10:20pm, and if the 10:20pm bus has a Delta_Time of 5 minutes, there are 25 minutes between bus arrivals at the stop.

Also note that based on a comment at https://planitmetro.com/2016/11/16/data-download-metrobus-vehicle-location-data/, the Delta_Time values don't appear to coincide with published bus schedules (e.g., the X2 departing every 8 minutes during peak hours).
```{r}

DeltTime_Ntile <- as.data.frame(AllDays_NewOrder$Delta_Time) %>% 
  mutate(Pctile = ntile(AllDays_NewOrder$Delta_Time, 100),
         MinR = min_rank(AllDays_NewOrder$Delta_Time),
         PctR = percent_rank(AllDays_NewOrder$Delta_Time),
         PctR_Round = round(PctR, 2)
        ) 

colnames(DeltTime_Ntile)[1] <- "Delta_Time"
str(DeltTime_Ntile)

DeltTime_Ntile_Rows <- nrow(DeltTime_Ntile)

View(tail(DeltTime_Ntile, 500))


DeltTime_Pctiles <- group_by(DeltTime_Ntile,
                             PctR_Round
                            ) %>% 
  summarise(
    MinDeltTimeAtPctile = min(Delta_Time),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / DeltTime_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(DeltTime_Pctiles)
DeltTime_Pctiles

# ~66% of rows are between 5 min late and 5 min early
nrow(filter(AllDays_NewOrder,
            Delta_Time >= -300 &
              Delta_Time <= 300
           )
    ) / nrow(AllDays_NewOrder)


# examples of weird large Delta_Times
View(filter(AllDays_NewOrder,
            Delta_Time < -4202 |
              Delta_Time > 1705
           ) %>% 
       arrange(desc(Delta_Time)
              )
    )

```


Investigation of Delta_Time (how early or late the bus is).

Delta_Time histogram.
```{r}

DeltTime_HistDen <- ggplot(AllDays_NewOrder, aes(x = (Delta_Time / 60),
                                                 y = ..density..
                                                )
                          ) +
  geom_histogram(binwidth = (5/60), fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_line(stat = "density", colour = "red") +
  coord_cartesian(xlim = c(-5, 5)) +
  xlab("Bus Lateness (min)") + 
  ylab("Density") + 
  #  theme(legend.position="none") + 
  ggtitle(expression(atop("Variation in How Early/Late a Bus Is",
                          atop(italic("(positive values are late arrivals)"),
                               ""
                              )
                         )
                    )
         )

DeltTime_HistDen

```


Investigation of Delta_Time (how early or late the bus is).

Delta_Time boxplot.
```{r}

# Count_Values is needed to display the medians on the box plots
Count_Values <- ddply(AllDays_NewOrder,
                      .(Event_Time_HrGroup),
                      summarise,
                      Value_Counts = median(Delta_Time / 60, na.rm = TRUE)
                     )

DeltTime_BoxPlot <- ggplot(AllDays_NewOrder,
                           aes(factor(Event_Time_HrGroup),
                               Delta_Time / 60,
                               fill = factor(Event_Time_HrGroup)
                              )
                          ) + 
  geom_boxplot(outlier.colour="red", notch=TRUE) + 
  # coord_cartesian(ylim = c(-300, 1200)) +
  coord_cartesian(ylim = c(-5, 20)) +
  geom_text(data = Count_Values,
            aes(y = Value_Counts,
                label = format(round(Value_Counts, digits = 1),
                               nsmall = 1
                              )
               ),
            size = 3,
            vjust = -0.5
           ) +
  xlab("Hour Group") + 
  ylab("Bus Lateness (minutes)") + 
  theme(legend.position="none", axis.text.x = element_text(angle=45)) + 
  #theme(legend.position="right", axis.text.x = element_blank()) + 
  ggtitle(expression(atop("How Early/Late is the Bus (by Hour Group)",
                          atop(italic("(positive values are late arrivals)"),
                               ""
                              )
                         )
                    )
         )

DeltTime_BoxPlot

```


Investigation of Delta_Time (how early or late the bus is).

Exploring "extreme" Delta_Times.  First let's get some "rank" stats.
```{r}

View(DeltTime_Pctiles)
DeltTime_Pctiles


DeltTimeAbs_Ntile <- as.data.frame(abs(AllDays_NewOrder$Delta_Time)) %>% 
  mutate(Pctile = ntile(abs(AllDays_NewOrder$Delta_Time), 100),
         MinR = min_rank(abs(AllDays_NewOrder$Delta_Time)),
         PctR = percent_rank(abs(AllDays_NewOrder$Delta_Time)),
         PctR_Round = round(PctR, 2)
        ) 

colnames(DeltTimeAbs_Ntile)[1] <- "Delta_Time_Abs"
str(DeltTimeAbs_Ntile)

DeltTimeAbs_Ntile_Rows <- nrow(DeltTimeAbs_Ntile)

View(tail(DeltTimeAbs_Ntile, 500))


DeltTimeAbs_Pctiles <- group_by(DeltTimeAbs_Ntile,
                                PctR_Round
                               ) %>% 
  summarise(
    MinDeltTimeAtPctile = min(Delta_Time_Abs),
    CntsAtPctile = n(),
    PctsAtPctile = CntsAtPctile / DeltTime_Ntile_Rows
  ) %>% 
  mutate(CumSumPAtP = cumsum(PctsAtPctile)
        )

View(DeltTimeAbs_Pctiles)
DeltTimeAbs_Pctiles

```


Investigation of Delta_Time (how early or late the bus is).

Exploring "extreme" Delta_Times.  Then let's calculate the percentage of buses that are 10 minutes (or more) late/early.
```{r}

HrGroup_DeltaTime_All <- group_by(AllDays_NewOrder,
                                  Event_Time_HrGroup
                                 ) %>% 
  summarise(EventAll_Cnt = n()
           )

str(HrGroup_DeltaTime_All)
View(HrGroup_DeltaTime_All)


HrGroup_DeltaTime_Above10Min <- filter(AllDays_NewOrder,
                                       abs(Delta_Time) >= 600
                                      ) %>% 
  group_by(Event_Time_HrGroup) %>% 
  summarise(EventAbove10_Cnt = n()
           )

str(HrGroup_DeltaTime_Above10Min)
View(HrGroup_DeltaTime_Above10Min)


HrGroup_DeltaTimeCompare <- inner_join(HrGroup_DeltaTime_Above10Min,
                                       HrGroup_DeltaTime_All,
                                       by = c("Event_Time_HrGroup" = "Event_Time_HrGroup")
                                      ) %>% 
  mutate(PctEventsAbove10 = EventAbove10_Cnt / EventAll_Cnt)

View(HrGroup_DeltaTimeCompare)

```


Investigation of Delta_Time (how early or late the bus is).

Quickly plot these "extreme" Delta_Times. 
```{r}

DeltTime_Above10_Cols <- ggplot(HrGroup_DeltaTimeCompare,
                                aes(factor(Event_Time_HrGroup),
                                    PctEventsAbove10
                                   )
                               ) +
  geom_col(fill = "lightblue", colour = "grey60", size = 0.2) +
  geom_text(aes(label = format(round(PctEventsAbove10, digits = 2),
                               nsmall = 2
                              )
               ),
            size = 3,
            nudge_y = (HrGroup_DeltaTimeCompare$PctEventsAbove10 * -0.1)
           ) +
  # coord_cartesian(xlim = c(-5, 5)) +
  xlab("Hour Group") + 
  ylab("Percent of All Bus Arrivals") +
  theme(legend.position="none", axis.text.x = element_text(angle=45)) +
  ggtitle(expression(atop("When is a Bus 10+ Minutes Late/Early"
                          # ,atop(italic("positive values are late arrivals"),
                          #      ""
                          #     )
                         )
                    )
         )

DeltTime_Above10_Cols

```


Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Correlation.
```{r}

DwellTDeltaT_Corr <- as.matrix(cor(x = AllDays_NewOrder$Dwell_Time2,
                                   y = AllDays_NewOrder$Delta_Time,
                                   use = "pairwise"
                                  )
                               )

DwellTDeltaT_Corr

```


Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Next, let's get a sample of data for plotting. Let's do this for the full dataset (AllDays_NewOrder).
```{r}

AllDays_NewOrder_10PctSamp <- sample_frac(AllDays_NewOrder, 0.1) %>% 
  select(Delta_Time,
         Dwell_Time2
        ) %>% 
  mutate(DataSet = "AllData")

str(AllDays_NewOrder_10PctSamp)

```


Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Let's also get a sample of data for plotting, but with a datset that removes outliers.
```{r}

View(DeltTime_Pctiles)
View(DwellTime2_Pctiles)

AllDays_NewOrder_NoExtremes_10PctSamp <- filter(AllDays_NewOrder,
                                                between(Delta_Time, -402, 1705) & # removes about 2% of Delta_Time values
                                                  between(Dwell_Time2, 1, 63)  # removes about 2% of Dwell_Time2 values
                                               ) %>% 
  sample_frac(0.1) %>% 
  select(Delta_Time,
         Dwell_Time2
        ) %>% 
  mutate(DataSet = "OutliersRemoved")

str(AllDays_NewOrder_NoExtremes_10PctSamp)

```


Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Plotting the data from the dataset that does not remove outliers.
```{r}

DwellTDeltaT_Scatter <- ggplot(AllDays_NewOrder_10PctSamp,
                               aes(Dwell_Time2, Delta_Time)
                              ) +
  geom_point(shape = 1, alpha = 0.5) +
  scale_shape(solid = FALSE) +
  geom_smooth(method = "lm", colour = "red") +
  # xlab("Time at Stop (sec)") + 
  # ylab("Lateness (sec)") +
  annotate(label = lm_eqn(df = AllDays_NewOrder_10PctSamp,
                          y = AllDays_NewOrder_10PctSamp$Delta_Time,
                          x = AllDays_NewOrder_10PctSamp$Dwell_Time2
                         ),
           x = 2200,
           y = 600,
           geom = "text",
           size = 3,
           colour = "red",
           parse = TRUE
          ) +
  labs(title = "Lateness vs Time at Stop",
       subtitle = "(no outliers removed)",
       x = "Time at Stop (sec)",
       y = "Lateness (sec)"
      )
  # ggtitle(expression(atop("Lateness vs Time at Stop"
  #                         ,atop(italic("(no outliers removed)"),
  #                               ""
  #                              )
  #                        )
  #                   )
  #        )
# +
#   geom_jitter()

DwellTDeltaT_Scatter

```


Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Plotting the data from the dataset that does remove outliers.
```{r}

DwellTDeltaT_Scatter_NoExtremes <- ggplot(AllDays_NewOrder_NoExtremes_10PctSamp,
                                          aes(Dwell_Time2, Delta_Time)
                                         ) +
  geom_point(shape = 1, alpha = 0.5) +
  scale_shape(solid = FALSE) +
  geom_smooth(method = "lm", colour = "blue") +
  # xlab("Time at Stop (sec)") + 
  # ylab("Lateness (sec)") +
  annotate(label = lm_eqn(df = AllDays_NewOrder_NoExtremes_10PctSamp,
                          y = AllDays_NewOrder_NoExtremes_10PctSamp$Delta_Time,
                          x = AllDays_NewOrder_NoExtremes_10PctSamp$Dwell_Time2
                         ),
           x = 50,
           y = -475,
           geom = "text",
           size = 3,
           colour = "blue",
           parse = TRUE
          ) +
  labs(title = "Lateness vs Time at Stop",
       subtitle = "(2% of outliers removed)",
       x = "Time at Stop (sec)",
       y = "Lateness (sec)"
      )
  # ggtitle(expression(atop("Lateness vs Time at Stop"
  #                         ,atop(italic("(2% of outliers removed)"),
  #                               ""
  #                              )
  #                        )
  #                   )
  #        )
# +
#   geom_jitter()

DwellTDeltaT_Scatter_NoExtremes

```


Quick investigation on the relationship between Dwell_Time2 (the time a bus is at a stop) and Delta_Time (how early/late the bus is).

Plotting the data from both datasets together.
```{r}

CombinedData <- rbind(AllDays_NewOrder_10PctSamp,
                      AllDays_NewOrder_NoExtremes_10PctSamp
                     )

CombinedData$DataSet <- factor(CombinedData$DataSet)

str(CombinedData)


DwellTDeltaT_Scatter_Combined <- ggplot(CombinedData,
                                        aes(x = Dwell_Time2,
                                            y = Delta_Time,
                                            colour = DataSet
                                           )
                                       ) +
  geom_point(shape = 1, alpha = 0.5) +
  scale_shape(solid = FALSE) +
  coord_cartesian(xlim = c(0, 500), ylim = c(-1000, 2000)
                 ) +
  geom_smooth(data = filter(CombinedData,
                            DataSet == "AllData"
                           ),
              method = "lm",
              colour = "red"
             ) +
  geom_smooth(data = filter(CombinedData,
                            DataSet == "OutliersRemoved"
                           ),
              method = "lm",
              colour = "blue"
             ) +
  # facet_wrap( ~ DataSet, ncol = 2) +
  annotate(label = lm_eqn(df = AllDays_NewOrder_10PctSamp,
                          y = AllDays_NewOrder_10PctSamp$Delta_Time,
                          x = AllDays_NewOrder_10PctSamp$Dwell_Time2
                         ),
           x = 300,
           y = -600,
           geom = "text",
           size = 3,
           colour = "red",
           parse = TRUE
          ) +
  annotate(label = lm_eqn(df = AllDays_NewOrder_NoExtremes_10PctSamp,
                          y = AllDays_NewOrder_NoExtremes_10PctSamp$Delta_Time,
                          x = AllDays_NewOrder_NoExtremes_10PctSamp$Dwell_Time2
                         ),
           x = 300,
           y = -800,
           geom = "text",
           size = 3,
           colour = "blue",
           parse = TRUE
          ) +
  theme(legend.position = "bottom") +
  labs(title = "Lateness vs Time at Stop",
       x = "Time at Stop (sec)",
       y = "Lateness (sec)"
      )
  # ggtitle(expression(atop("Lateness vs Time at Stop"
                          # ,atop(italic("2% of outliers removed"),
                          #       ""
                          #      )
         #                 )
         #            )
         # )
# +
#   geom_jitter()

DwellTDeltaT_Scatter_Combined

# rm(AllDays_StopIDNew, lat, LL_Stats, LL_Stats_UnqLatLng, LL_StatsZips, lng, WaitData_DayPull, WaitData_RoutePull, Zip, Zips_All, Zips0, Zips1, Zips2, Zips3, Zips4, Zips5, Zips6, Zips7, Zips8, Zips9, Zips10, APIData1, BasePath, i, k, pages1, url_1, url_2, url_3, username)

```



